To see the other types of publications on this topic, follow the link: Intelligent recommendation systems.
Journal articles on the topic 'Intelligent recommendation systems'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Intelligent recommendation systems.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Resnick, Marc L., Sheryda Pompa, Isaac Korn, and Omar Castillo. "Persuasive Design Through Intelligent Recommendation Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 13 (2004): 1503–7. http://dx.doi.org/10.1177/154193120404801307.
Full text
2
Zunying, Xie. "Analysis of Intelligent Recommendation Systems and Consumer Behavior Theories on E-Commerce Platforms." Philosophy and Social Science 1, no. 6 (2024): 10–15. https://doi.org/10.62381/p243602.
Full textAbstract:
This study explores the interplay between intelligent recommendation systems and consumer behavior theories on e-commerce platforms. With the rapid growth of e-commerce, intelligent recommendation systems have become vital tools for enhancing user experience and boosting sales. While much literature addresses the technical implementation and algorithm optimization of these systems, research from the perspective of consumer behavior theory is limited. This paper first reviews the fundamental principles and technological evolution of recommendation systems, summarizing common algorithms and their specific applications in e-commerce. Next, from the viewpoint of consumer behavior theory, it systematically analyzes the impact of recommendation systems on consumer decision-making processes, purchasing behavior, and user satisfaction. It examines how recommendation systems influence consumer decisions and purchase intentions through mechanisms such as information overload, choice simplification, social recognition, and a sense of belonging. Additionally, the paper evaluates the applicability and effectiveness of various types of recommendation systems (e. g., personalized, contextual, and social recommendations) in different consumption scenarios. Findings indicate that intelligent recommendation systems significantly enhance shopping experiences and satisfaction while profoundly affecting purchasing decisions. This research provides theoretical guidance for designing recommendation systems on e-commerce platforms and offers new perspectives and methods for future consumer behavior studies. By delving into the complex interactions between recommendation systems and consumer behavior, it provides valuable insights for the intelligent transformation and user experience optimization of e-commerce platforms.
3
Sohel, Shaik, Vanukuri Manideepa, Alla Sai Pavan, Danaboina Vamsi Krishna, and KRMC Sekhar. "EMUS: An Intelligent Music Recommendation System." International Journal of Multidisciplinary Research and Growth Evaluation. 6, no. 2 (2025): 751–55. https://doi.org/10.54660/.ijmrge.2025.6.2.751-755.
Full textAbstract:
Music plays a prominent role in various aspects of human life, culture, and society by influencing emotions, strengthening social bonds, preserving traditions, and shaping personal and collective identities. As AI emerges as a powerful tool to automate various tasks, music recommendation systems have become an integral part of this transformation. These systems automatically generate personalized music playlists for users based on their mood and listening behavior. By analyzing factors like facial expressions, voice tone, text input, and listening history, AI-driven music recommendation systems identify the user’s emotional state and suggest songs that match or enhance their mood. Emotion-based music recommendation systems significantly enhance the way people experience music by improving emotional well-being, boosting user engagement, and broadening musical preferences. In this work, we propose an application called EMUS, an intelligent music recommendation system designed to suggest music based on the user’s emotional state.
4
Iklassova, K. Е., A. K. Shaikhanova, M. Zh Bazarova, R. M. Tashibayev, and A. S. Kazanbayeva. "REVIEW OF RECOMMENDER SYSTEMS: MODELS AND PROSPECTS FOR USE IN EDUCATIONAL PLATFORMS." Bulletin of Shakarim University. Technical Sciences, no. 1(17) (March 29, 2025): 12–20. https://doi.org/10.53360/2788-7995-2025-1(17)-2.
Full textAbstract:
Recommendation systems play a key role in the digital environment, providing personalized recommendations in online stores, streaming services, social networks, and educational platforms. This paper presents a comprehensive review of recommendation system models, including content and collaborative filtering, hybrid approaches, and state-of-the-art algorithms based on deep learning, reinforcement learning, and graph neural networks. The advantages and disadvantages of different methods, their accuracy, performance, scalability and adaptability to new data are analyzed. The main challenges such as the cold-start problem, data sparsity, bias of algorithms, the need for explainability of recommendations and privacy assurance are reviewed. Special attention is paid to the prospects of implementing recommendation systems in educational platforms. The importance of using hybrid and intelligent systems to effectively analyze user data and build recommendations tailored to individual needs is emphasized. The conclusion is drawn about further development of recommendation systems, which will be associated with the integration of the latest artificial intelligence technologies, optimization of computational resources and expansion of their application area in various digital ecosystems. The work can be useful for researchers, developers and practitioners working in the field of artificial intelligence and educational technologies.
5
Hirolikar, D. S., Ajinkya Satuse, Omkar Bhalerao, Pavan Pawar, and Hrithik Thorat. "Intelligent Movie Recommendation System Using AI and ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 611–22. http://dx.doi.org/10.22214/ijraset.2022.42255.
Full textAbstract:
Abstract: Recommender system are systems which provide you with a similar type of products or solutions and results, you are looking for. For example, if you go to a Clothing shop, you ask for a T-shirt with different designs or different colors, Then the shopkeeper recommends you with different colors. This recommending task for websites is done by recommending systems. A recommendation engine uses several algorithms to filter data and then recommends the most relevant items to consumers. A Movie Recommender system will recommend the most relevant and connected movie for the given category of search, if a user visits a movie site for the first time, the site will have no previous history of that user. In such cases, the user can search for their movie recommendations based on genre, year of release, director or actor and their favorite movie itself to get a new movie recommendation. Keywords: Movie Recommendation Systems, Content-Based Filtering, Movie recommendation, machine learning project
6
Jia, Yu Bo, Qian Qian Ding, Dan Li Liu, Jian Feng Zhang, and Yun Long Zhang. "Collaborative Filtering Recommendation Technology Based on Genetic Algorithm." Applied Mechanics and Materials 599-601 (August 2014): 1446–52. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1446.
Full text
7
Huang, Zhao, and Pavel Stakhiyevich. "A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users." Complexity 2021 (February 27, 2021): 1–19. http://dx.doi.org/10.1155/2021/8826833.
Full textAbstract:
Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.
8
Wang, Peilian, and Hui Xie. "Application and Exploration of Artificial Intelligence in Teaching and Learning in Private Colleges and Universities." World Journal of Education and Humanities 6, no. 3 (2024): p26. http://dx.doi.org/10.22158/wjeh.v6n3p26.
Full textAbstract:
The rapid advancement of artificial intelligence technology across various sectors has sparked profound transformation in the field of education, particularly in the realms of pedagogy and administration within private higher education institutions. The discourse delves into the specific applications of AI educational aids in both classroom instruction and post-class learning, encompassing intelligent tutoring systems, virtual laboratories, and personalized learning recommendation systems, among others. Furthermore, it addresses the utilization of AI-driven question-answering systems, automated homework grading systems, and speech recognition technology. In terms of educational administration, it investigates the integration of intelligent student information systems, learning trajectory tracking systems, as well as course resource recommendation systems and teaching evaluation systems. The aim of the article is to furnish educators with valuable insights to enhance teaching quality and administrative efficiency, thereby promoting the intelligent development of private higher education institutions.
9
Chunduri, Sreya, Harry Raj, and Narendra V. G. "Intelligent Systems for Crop Recommendation using Machine Learning." WSEAS TRANSACTIONS ON COMPUTERS 24 (January 10, 2025): 14–19. https://doi.org/10.37394/23205.2025.24.2.
Full textAbstract:
Given the soil and climate, information is of utmost importance in predicting which crop is best suited. Crops can now be grown with higher precision by analyzing data regarding temperature, humidity, soil conditions, and the chemical makeup of the soil, all of which impact crop growth. This is one facet of Precision Agriculture. Precision agriculture is a contemporary farming approach that uses scientific findings on the types, properties, and yields of soil. It guides farmers in selecting the most suitable crops tailored to their specific site conditions, reducing the chance of making unsuitable crop selections and ultimately helping raise overall productivity. The proposed work offers a web application that assists in classifying 22 crops based on various soil and environmental factors using two algorithms: SVM and Decision Trees. It analyzes the classifiers' accuracy using two performance metrics: the confusion matrix and the accuracy score. Farmers are better able to decide on the farming strategy they wish to use after utilizing the application.
10
Cheng, Xiao, and Guochao Peng. "Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems." Journal of Global Information Management 32, no. 1 (2024): 1–22. http://dx.doi.org/10.4018/jgim.352857.
Full textAbstract:
Through an exploration of the underlying mechanisms driving users' algorithmic avoidance in intelligent recommendation systems, this study aims to facilitate a positive interaction between users and technology, providing theoretical guidance for the efficient operations of enterprises using intelligent recommendation systems. The research integrates the theories of information ecology and psychological resistance, establishing a model of influencing factors on users' algorithmic avoidance in intelligent recommendation systems. Utilizing a structural equation model, the study conducts analysis and validation on data collected from 506 questionnaires. The findings reveal that algorithmic transparency and perceived manipulation significantly impact the users' algorithmic avoidance in intelligent recommendation systems. The sense of being manipulated emerges as a crucial psychological factor leading to algorithmic avoidance, playing a complete mediating role in the influence of information quality, homogeneous recommendation, and algorithmic transparency on algorithmic avoidance.
11
Tao, Dingxin. "The Analysis of Recommendation Algorithms in Different Domains and Future Development Trends." Applied and Computational Engineering 145, no. 1 (2025): 22–28. https://doi.org/10.54254/2755-2721/2025.21893.
Full textAbstract:
Recommendation algorithms are a crucial research direction in the fields of artificial intelligence and data science, with widespread applications in e-commerce, streaming media, education, healthcare, and social networks. The demand for accurate and personalized information has driven the development of recommendation systems. However, different application scenarios place varying emphases on recommendation algorithms. For instance, e-commerce focuses on conversion rates, social platforms emphasize user relationship expansion, and the healthcare sector prioritizes accuracy and privacy protection. Consequently, optimizing recommendation algorithms based on industry-specific characteristics has become a key research focus. This paper summarizes the core technologies of recommendation algorithms and their applications across different domains. It also analyzes current challenges such as data sparsity, the cold start problem, and privacy protection, along with corresponding countermeasures. To address these issues, researchers have proposed optimization methods that integrate deep learning and reinforcement learning, as well as improvements such as cross-domain data fusion and user intent modeling. Furthermore, future trends in recommendation systems include cross-domain recommendations, enhanced privacy protection techniques, improved interpretability, and the adoption of federated learning to ensure user data security while enhancing recommendation quality. With the continuous advancement of artificial intelligence, recommendation systems will become more intelligent, personalized, and secure, providing users with more accurate and efficient recommendation services.
12
Mu, Juntong. "The Application and Effect of Intelligent Marketing Technology and Personalized Recommendation System in E-commerce." Frontiers in Computing and Intelligent Systems 5, no. 1 (2023): 1–4. http://dx.doi.org/10.54097/fcis.v5i1.11533.
Full textAbstract:
Intelligent marketing technology and personalized recommendation systems play an important role in e-commerce, which has a significant impact on improving user experience and promoting sales growth. This article mainly explores the application and effectiveness of intelligent marketing technology and personalized recommendation systems, as well as their value and impact on e-commerce. In the future, with the continuous progress of technology, intelligent marketing technology and personalized recommendation systems will play a broader and deeper role in the field of e-commerce. Intelligent marketing technology and personalized recommendation systems have shown widespread application and significant effects in e-commerce. They enhance user experience and promote sales growth, which is of great significance for the development and promotion of e-commerce.
13
Cui, Xiaoyue. "An Adaptive Recommendation Algorithm of Intelligent Clothing Design Elements Based on Large Database." Mobile Information Systems 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/3334047.
Full textAbstract:
In the recent years, the developmental speed of intelligent technology continues to accelerate, and the research on the actual needs of users is also in depth. From the current situation of the clothing industry, how to combine artificial intelligence (AI) technology with clothing fashion has become the focus of customer’s attention. The application of intelligent clothing matching recommendation system (online) can effectively meet the needs of customers in dressing matching, so as to save a lot of time and energy (offline). With the maturity of artificial intelligence, machine learning, and other emerging computational technologies, the intelligent clothing matching system has laid a solid foundation. In this paper, several intelligent clothing matching recommendation systems that have been applied at present are deeply analyzed. Moreover, the basic algorithms and key technologies are elaborated in detail. In addition, the future research direction is found, so that the clothing matching recommendation system can be more personalized, and the comprehensive function is greatly improved in order to bring more ideal benefits.
14
Xu, Kangming, Huiming Zhou, Haotian Zheng, Mingwei Zhu, and Qi Xin. "Intelligent classification and personalized recommendation of E-commerce products based on machine learning." Applied and Computational Engineering 64, no. 1 (2024): 143–49. http://dx.doi.org/10.54254/2755-2721/64/20241365.
Full textAbstract:
With the rapid evolution of the Internet and the exponential proliferation of information, users encounter information overload and the conundrum of choice. Personalized recommendation systems play a pivotal role in alleviating this burden by aiding users in filtering and selecting information tailored to their preferences and requirements. This paper undertakes a comparative analysis between the operational mechanisms of traditional e-commerce commodity classification systems and personalized recommendation systems. It delineates the significance and application of personalized recommendation systems across e-commerce, content information, and media domains. Furthermore, it delves into the challenges confronting personalized recommendation systems in e-commerce, including data privacy, algorithmic bias, scalability, and the cold start problem. Strategies to address these challenges are elucidated. Subsequently, the paper outlines a personalized recommendation system leveraging the BERT model and nearest neighbor algorithm, specifically tailored to address the exigencies of the eBay e-commerce platform. The efficacy of this recommendation system is substantiated through manual evaluation, and a practical application operational guide and structured output recommendation results are furnished to ensure the system's operability and scalability.
15
Chen, Qing Zhang, Yu Jie Pei, Yan Jin, and Li Yan Zhang. "Research on Intelligent Recommendation Method and its Application on Internet Bookstore." Advanced Materials Research 121-122 (June 2010): 447–52. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.447.
Full textAbstract:
As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.
16
Malikireddy, Sai Kiran Reddy. "Revolutionizing Product Recommendations with Generative AI: Context-Aware Personalization at Scale." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem40434.
Full textAbstract:
Generative Artificial Intelligence (GenAI) is poised to transform the product recommendation landscape by bridging the gap between user intent and personalized discovery. Traditional recommendation systems rely heavily on collaborative filtering, content-based algorithms, or hybrid models, often constrained by sparse data and limited contextual understanding. GenAI introduces a paradigm shift by leveraging advanced transformer-based architectures and multimodal embeddings to deliver highly contextual, dynamic, and explainable recommendations at scale. This paper explores the use of GenAI for product recommendation systems, focusing on its ability to generate rich, context- aware interactions that mimic human-like personalization. By fine-tuning pre-trained language models on domain- specific product catalogs and user behavior data, we demonstrate how GenAI can synthesize user preferences into coherent narratives, predict latent needs, and suggest products that align with evolving trends. Additionally, we propose a novel “Recommendation Dialogue Model” that integrates natural language prompts with visual and textual content to provide seamless, conversational shopping experiences. Our experiments, conducted on benchmark datasets and real-world e-commerce platforms, show that GenAI-based systems outperform traditional models in precision, recall, and customer satisfaction metrics. Furthermore, we address challenges such as mitigating bias, ensuring diversity in recommendations, and preserving privacy through federated learning approaches. By reimagining product discovery as a generative process, this work highlights the transformative potential of GenAI to create hyper-personalized, interactive, and engaging recommendation systems that redefine how users find and connect with products. The implications extend to e-commerce, media streaming, and beyond, offering a blueprint for the next generation of intelligent systems. Keywords: Generative Artificial Intelligence, Product Recommendations, Transformer Architectures, Multimodal Embeddings, Recommendation Dialogue Model, Natural Language Processing, Contextual Understanding, Federated Learning, Privacy Preservation
17
Ramzan, Bushra, Imran Sarwar Bajwa, Noreen Jamil, et al. "An Intelligent Data Analysis for Recommendation Systems Using Machine Learning." Scientific Programming 2019 (October 31, 2019): 1–20. http://dx.doi.org/10.1155/2019/5941096.
Full textAbstract:
In recent times, selection of a suitable hotel location and reservation of accommodation have become a critical issue for the travelers. The online hotel search has been increased at a very fast pace and became very time-consuming due to the presence of huge amount of online information. Recommender systems (RSs) are getting importance due to their significance in making decisions and providing detailed information about the required product or a service. To acquire the hotel recommendations while dealing with textual hotel reviews, numerical ranks, votes, ratings, and number of video views have become difficult. To generate true recommendations, we have proposed an intelligent approach which also deals with large-sized heterogeneous data to fulfill the needs of the potential customers. The collaborative filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion-based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis, and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommends hotel class based on guest type using fuzzy rules. Different experiments are performed over the real-world datasets obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated, and the results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.
18
Ramzan, Bushra, Imran Bajwa, Rafaqut Kazmi, and Shabana Ramzan. "An Intelligent Data Analytics based Model Driven Recommendation System." JUCS - Journal of Universal Computer Science 25, no. (10) (2019): 1353–72. https://doi.org/10.3217/jucs-025-10-1353.
Full textAbstract:
The recommendation systems are getting important due to their significance in decision making, social and economic impact on customers and getting detailed information relevant to a required product or a service. A challenge in getting true recommendations in terms of relevance is the heterogenous nature of data (likes, ratings, reviews, etc.) that a recommendation engine has to cope with. This paper presents an intelligent approach to handle heterogeneous and large-sized data of user reviews and generate true recommendations for the future customers. The proposed approach makes use of Apache Cassandra to efficiently store data (such as customer reviews, feedback of hotel customers) having context properties such as awareness and knowledge of the tourists, personal preferences (such as ratings, likes, etc.) and location of the users. This system consists of three main components: the web front-end, the data storage and the recommendation engine to gain recommendations efficiently. The recommendation engine is relying on Euclidean distance and Collaborative Filtering (CF) to measure similarities in users' review or items' features. Our hotel recommender approach has bifold contribution as it has ability to handle heterogeneous data with the help of big data platform and it also provides accurate and true recommendations.
19
Yang, Siran. "Design and Application of Intelligent Recommender Systems in Vocational Rehabilitation." Transactions on Computer Science and Intelligent Systems Research 6 (October 17, 2024): 196–203. http://dx.doi.org/10.62051/n4g3zf31.
Full textAbstract:
With the rapid development of information technology, the application of intelligent recommender systems in various fields has become more and more extensive, and vocational rehabilitation, as a field focusing on personalized services, faces the challenge of how to effectively use big data and intelligent algorithms to enhance the rehabilitation effect. This paper discusses the design principles and key technologies of intelligent recommender system in vocational rehabilitation, combining data-driven recommendation algorithms with users' personalized needs to achieve accurate recommendation of vocational rehabilitation programs. Through the optimization of system architecture and functional implementation, the intelligent recommender system shows remarkable effects in practical application, effectively improving the decision-making efficiency and user satisfaction in the rehabilitation process. Finally, this paper gives an outlook on the future development of the intelligent recommendation system in the field of vocational rehabilitation, and proposes possible research directions and application expansion paths.
20
Salahli, Mehmet Ali, Tokay Gasimzade, Flora Alasgarova, and Akber Guliyev. "The Use of Predictive Models in Intelligent Recommendation Systems." Procedia Computer Science 102 (2016): 515–19. http://dx.doi.org/10.1016/j.procs.2016.09.436.
Full text
21
Zhang, Bohan. "Artificial Intelligence in Marketing." Transactions on Social Science, Education and Humanities Research 9 (July 8, 2024): 181–87. http://dx.doi.org/10.62051/s4y73e41.
Full textAbstract:
This thesis explores the application of artificial intelligence in marketing and the trends, challenges and opportunities of digital transformation. Firstly, it introduces the definition, development history and main technology and application areas of artificial intelligence. Then, it analyses the overview of the application of AI in the business field, including personalised recommendation, intelligent customer service and other aspects. Subsequently, how AI has changed marketing strategies and practices is explored and compared with traditional approaches. In terms of specific applications, the role and benefits of personalised recommendation systems, intelligent customer service systems, etc. are highlighted. In addition, the application of data-driven market trend analyses, intelligent predictive models, etc. in marketing is examined. Finally, the impact of AI and digital transformation on marketing is summarised, highlighting the need for companies to strengthen data security and management, and to grasp the opportunities of technological development to achieve sustainable development and competitive advantage.
22
Bhuiyan, Md Momen, Donghan Hu, Andrew Jelson, Tanushree Mitra, and Sang Won Lee. "Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank." Proceedings of the ACM on Human-Computer Interaction 8, CSCW2 (2024): 1–23. http://dx.doi.org/10.1145/3687041.
Full textAbstract:
Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.
23
Liu, Yingchia, Yang Xu, and Shiji Zhou. "Enhancing User Experience through Machine Learning-Based Personalized Recommendation Systems: Behavior Data-Driven UI Design." Applied and Computational Engineering 112, no. 1 (2024): 42–46. http://dx.doi.org/10.54254/2755-2721/2024.17905.
Full textAbstract:
The application of artificial intelligence (AI) continues to expand across various industries, especially in enhancing user experience and optimizing business processes. Through deep learning and machine learning algorithms, companies are able to analyze user behavior data and provide personalized recommendations, which effectively improve customer satisfaction and loyalty. This data-driven approach enables businesses to stand out in a highly competitive market. This paper explores the key role of machine learning-based personalized recommendation systems in improving user experience and highlights the importance of behavioral data-driven UI design for business success. Research shows that successful recommendation systems not only rely on advanced technology applications, but also need to deeply understand user needs to optimize user interface design and promote effective user interaction. As technology continues to advance, personalized recommendation systems will become more intelligent, and companies should actively explore these innovative ways to increase user engagement and brand loyalty to achieve sustainable business growth.
24
J. Uma, V. Arun Kumar, R. Karthikeyan, V. Lavanya, and P. Priyadharshini. "Integration of Artificial Intelligence into Software Component Reuse: An Overview of Software Intelligence." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1086–88. https://doi.org/10.47392/irjaem.2025.0178.
Full textAbstract:
Artificial Intelligence (AI) is transforming software component reuse by enhancing automation, efficiency, and intelligent retrieval of reusable software artifacts. Traditional reuse methods face challenges in retrieving, classifying, and recommending components due to the complexity of software repositories. AI-driven techniques such as machine learning (ML), natural language processing (NLP), and knowledge graphs help overcome these limitations by enabling intelligent categorization and recommendation. Software Intelligence (SI) enhances reuse by employing data mining techniques to extract patterns from large repositories. A centralized AI-powered repository improves component discovery, allowing developers to find and integrate relevant components efficiently. NLP enhances semantic understanding, enabling better classification and retrieval of software components. However, AI-driven software reuse presents challenges, including data quality, interoperability, and AI model integration. Future research should focus on improving automation through deep learning, refining repository structures, and optimizing recommendation systems. Ethical concerns, such as bias in AI recommendations and intellectual property rights, must also be addressed.
25
Huang, Xiao, Pengjie Ren, Zhaochun Ren, et al. "Report on the international workshop on natural language processing for recommendations (NLP4REC 2020) workshop held at WSDM 2020." ACM SIGIR Forum 54, no. 1 (2020): 1–5. http://dx.doi.org/10.1145/3451964.3451970.
Full textAbstract:
This paper summarizes the outcomes of the International Workshop on Natural Language Processing for Recommendations (NLP4REC 2020), held in Houston, USA, on February 7, 2020, during WSDM 2020. The purpose of this workshop was to explore the potential research topics and industrial applications in leveraging natural language processing techniques to tackle the challenges in constructing more intelligent recommender systems. Specific topics included, but were not limited to knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation.
26
Yu, Guo, Jing Qin, Ziqian Cheng, Zihao Li, and Saiyu Quan. "The Transformation of Intelligent Tourism Planning through the "Zibo Barbecue" Craze." Journal of Big Data and Computing 2, no. 1 (2024): 50–55. http://dx.doi.org/10.62517/jbdc.202401107.
Full textAbstract:
This article discusses the application and development of intelligent tourism planning in the barbecue frenzy in Zibo. Intelligent tourism planning offers advantages such as personalized customization, real-time accuracy, recommendation suggestions, and provides tourists with a more convenient and efficient travel experience. In the context of the barbecue frenzy in Zibo, intelligent guided services, recommendation systems, and booking services have become essential components, offering tourists a more intelligent and personalized tourism experience. In this background, intelligent tourism planning continues to evolve, utilizing precise recommendations, interactive experiences, and intelligent marketing promotion to further enhance the travel experience and meet the needs of tourists. In conclusion, intelligent tourism planning demonstrates immense potential and development opportunities within the barbecue frenzy in Zibo, injecting new vitality and possibilities into the tourism industry. With the continuous progress and application of intelligent technologies, it is believed that intelligent tourism planning will become an important trend in the travel industry, providing tourists with a richer and more convenient travel experience.
27
Yang, Weiwei. "Personalized Intelligent Recommendation Algorithm Design for Book Services Based on Deep Learning." Wireless Communications and Mobile Computing 2022 (March 10, 2022): 1–8. http://dx.doi.org/10.1155/2022/9203665.
Full textAbstract:
Machine learning is one of the important branches of artificial intelligence, which provides new technical means for analyzing users, understanding users, and gaining insight into users. The framework of the personalized intelligent service model for libraries based on user portraits is proposed, and intelligent technologies such as machine learning are applied to analyze and mine users’ big data, build user portraits, associate users and resources with user portraits, and provide personalized intelligent services for users. The study gives a case study of book recommendation by firstly extracting users’ personalized interests, then applying the plain Bayesian algorithm to discover users’ current personalized potential demands for books, and finally providing users with personalized intelligent book recommendation services that match their interests and mainly meet their potential demands.
28
Kumar, Sachin. "INTELLIGENT MOVIE RECOMMENDER FRAMEWORK BASED ON CONTENT-BASED & COLLABORATIVE FILTERING ASSISTED WITH SENTIMENT ANALYSIS." International Journal of Advanced Research in Computer Science 14, no. 03 (2023): 108–13. http://dx.doi.org/10.26483/ijarcs.v14i3.6979.
Full textAbstract:
Recommendation systems for rating movies and forming opinion have grown exponentially in popularity, they make it very convenient for consumers to select films that suit their tastes. However, traditional recommendation systems often rely solely on user ratings or reviews, which may not accurately reflect the user's true feelings about a movie. To address this issue, sentiment analysis has been proposed as a more reliable method for capturing emotional information about movies. In this research paper, we propose a novel movie recommendation system that combines sentiment analysis with collaborative filtering and content-based methods. Our system is designed to provide accurate and timely recommendations to mobile users based on their preferences, reviews, and emotions. We evaluate the performance of our system using real-world data and demonstrate its effectiveness in improving the accuracy and timeliness of movie recommendations. In this research paper, we propose a comparative study of three popular movie recommendation techniques: content-based filtering, collaborative filtering, and sentiment analysis. We aim to evaluate the effectiveness of each approach in providing accurate and personalized movie recommendations. Work done has utilized MovieLens dataset for this study, which is a popular benchmark dataset for assessing movie recommendation systems. The dataset includes almost 100,000 ratings, ranging from 1 to 5, from 943 individuals on 1,682 films. Each rating has a timestamp, a special user ID, and a movie ID. The collection also contains details about each movie's genre. To prepare the dataset for our experiments, we first performed some data cleaning and pre-processing. We removed any duplicate ratings, and any movies or users with a low number of ratings were also removed. We also performed some feature engineering to extract relevant features from the raw data, such as movie genres, user demographics, and movie release dates. Our tests on the Movielens dataset show that the system we've suggested works well. Precision, recall, and F1-score were some of the measures we used to assess the system's performance. We discovered that it performed better than conventional recommendation systems that just use content-based or collaborative filtering techniques. A better user experience was produced with the use of sentiment analysis, which improved the accuracy and promptness of suggestions. Algorithm designed achieves a precision of 0.875, Recall of 0.0435, F1 Score of 0.0828, Accuracy of 0.9889, RMSE of 0.407, and F measure of 0.1813.
29
Medani, Mohamed, Shtwai Alsubai, Hong Min, Ashit Kumar Dutta, and Mohd Anjum. "Discriminant Input Processing Scheme for Self-Assisted Intelligent Healthcare Systems." Bioengineering 11, no. 7 (2024): 715. http://dx.doi.org/10.3390/bioengineering11070715.
Full textAbstract:
Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method’s scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management’s accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations.
30
Shao, Ruihua. "Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System." Advances in Multimedia 2022 (July 14, 2022): 1–13. http://dx.doi.org/10.1155/2022/7860718.
Full textAbstract:
In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.
31
Liu, Bo. "Based on intelligent advertising recommendation and abnormal advertising monitoring system in the field of machine learning." International Journal of Computer Science and Information Technology 1, no. 1 (2023): 17–23. http://dx.doi.org/10.62051/ijcsit.v1n1.03.
Full textAbstract:
With the rapid development of the Internet, the scale of the online advertising market has expanded rapidly, and display advertising has become the most popular means of publicity. Accurate advertising recommendation is the guarantee of Internet platform revenue, and accurate advertising click rate prediction is the premise of accurate recommendation and abnormal advertising detection. Therefore, such monitoring and recommendation can be achieved through machine learning combined with artificial intelligence, and the application of intelligent AD recommendation systems and abnormal AD monitoring in the field of machine learning represents a complex integration of technologies to improve the precision and effectiveness of digital marketing strategies. Intelligent AD recommendation systems utilize advanced machine learning algorithms to analyze user behavior and preferences to deliver tailored AD content. These systems leverage vast amounts of user data, including browsing history, purchase history, and engagement metrics, to predict and present the most relevant ads. This paper analyzes the data mining in machine learning algorithms and the real-time online recommendation algorithm of Gaussian process, and analyzes the abnormal advertising monitoring system for maintaining the integrity and efficiency of advertising campaigns. By using machine learning technology for pattern recognition and anomaly detection, various measures and indicators of advertising campaigns can be monitored in a vigilant manner.
32
Wei, Hong, and Zhiyong Li. "Intelligent Recommendation Method of Mobile Wireless Communication Information Based on Speech Recognition Technology Under Strong Multipath Interference." International Journal of Information Security and Privacy 16, no. 2 (2022): 1–18. http://dx.doi.org/10.4018/ijisp.308308.
Full textAbstract:
With the rapid development of the Internet, it is more and more difficult to get interested information from the mass of information. In order to alleviate the problem of data sparsity, this paper proposes an intelligent recommender of mobile wireless communication information based on speech recognition technology under strong multipath interference with the help of speech recognition technology. According to different types of mobile wireless communication information, the recommended path is selected, and the information is denoised to reduce the interference value of mobile wireless communication information intelligent recommendation and ensure the recommendation effect. Finally, the experimental results show that the proposed intelligent recommendation method of mobile wireless communication information based on speech recognition technology under strong multipath interference has high prediction accuracy and resource diversity, strong excavation ability, and good recommendation effect.
33
Adomavicius, Gediminas, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. "Context-Aware Recommender Systems." AI Magazine 32, no. 3 (2011): 67. http://dx.doi.org/10.1609/aimag.v32i3.2364.
Full textAbstract:
Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.
34
K, DanielRaj. "Intelligent Crop Recommendation System Using Machine Learning Approach." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5675–81. https://doi.org/10.22214/ijraset.2025.69677.
Full textAbstract:
The agricultural sector in India, despite its status as a leading producer globally, grapples with low farm productivity, resulting in diminished incomes for farmers. Addressing this challenge requires a strategic approach centered around increasing productivity, thereby enhancing farmer livelihoods. Crucially, farmers must be equipped with the knowledge of which crops are best suited to their specific plots of land to optimize yield potential. This entails consideration of various factors such as temperature, humidity, soil pH, rainfall patterns, and nutrient composition. However, many farmers lack access to this vital information, leaving them uncertain about which crops to cultivate for maximum yield and profit. Thus, the implementation of crop recommendation systems powered by machine learning algorithms presents a promising solution. By leveraging data on environmental conditions and soil properties, these systems can accurately predict suitable crop choices for individual farms, empowering farmers to make informed decisions and ultimately improve their productivity and income levels.
35
Priyanka Singla. "An Intelligent Job Recommendation System based on Semantic Embeddings and Machine Learning." Journal of Information Systems Engineering and Management 10, no. 5s (2025): 520–42. https://doi.org/10.52783/jisem.v10i5s.681.
Full textAbstract:
To address the shortcomings in existing approaches of job recommendation systems, this paper proposes a novel machine-learning-based job recommendation system that performs bi-directional matching for dynamic and accurate recommendations. The proposed approach generates ideal job recommendations for a targeted Curriculum Vitae (CV) and vice versa. Unlike previous approaches, the proposed approach incorporates natural language processing (NLP) techniques to extract linguistic features such as Bag of Words (BoW), n-grams, TF-IDF, and Parts-of-Speech (PoS) tag and build a rich feature set. These features are further analyzed using semantic embeddings, enabling robust job matching. Experiments were performed to validate the performance of the proposed approach. The designed system is validated on various real-world datasets, overcoming the dataset size limitations of prior works. Due to combination of semantic embeddings, machine learning, and various similarity measures, this approach demonstrates the potential to deliver reliable, explainable, and ideal job recommendations, addressing the challenges of static and false outputs in existing systems.
36
Cheng, Saiya. "The Influence of Information Exposure on Teenagers’ Privacy Protection Behaviour under the Intelligent Recommendation System." International Journal of Education and Humanities 17, no. 3 (2024): 367–74. https://doi.org/10.54097/481snk20.
Full textAbstract:
This study focuses on the influence of information exposure under an intelligent recommendation system on teenagers’ privacy protection behaviour. By employing the Communication Privacy Management (CPM) theory, we examine how information exposure through intelligent recommendation systems influences teenagers’ attitudes and actions towards privacy. The results of this study emphasize the need for the CPM advocated by Petronio [1] and the role of various factors in influencing privacy protection behaviour. The study also explores teenagers’ privacy protection behaviours on social media. Through quantitative research, this study provides a perspective on the interaction among adolescents’ digital literacy, privacy issues and behavioural responses in the context of intelligent recommendation systems.
37
Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.
Full textAbstract:
In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).
38
Kozierkiewicz-Hetmańska, Adrianna. "A METHOD FOR SCENARIO RECOMMENDATION IN INTELLIGENT E-LEARNING SYSTEMS." Cybernetics and Systems 42, no. 2 (2011): 82–99. http://dx.doi.org/10.1080/01969722.2011.541208.
Full text
39
VURUSKAN, ARZU, GOKHAN DEMİRKİRAN, ENDER BULGUN, TURKER INCE, and CUNEYT GUZELIS. "Design of an interactive fashion recommendation platform with intelligent systems." Industria Textila 75, no. 02 (2024): 177–84. http://dx.doi.org/10.35530/it.075.02.202312.
Full textAbstract:
With the increase in customer expectations in online fashion sales, greater integration of fashion recommender systems (RSs) allows more personalization. Design decisions rely on personal taste, as well as many other external influences, such as trends and social media, making it challenging to adapt intelligent systems for the fashion industry. Different methods for recommending personalized fashion items have been proposed, however, the literature still lacks an approach for recommending expert-suggested and personalized items. In this research, an interactive web-based platform is developed to support personalized fashion styling, focusing on users with diverse body shapes. To merge the user’s taste and the expert’s suggestion, the proposed methodology in this research combines genetic algorithms and machine learning techniques allowing the system to access expert knowledge (including external influences) and incremental learning capability, by adapting to the user preferences that unfold during interaction with the system.
40
Li, Qi, Rui Miao, Jie Zhang, and Xiaoxu Deng. "An Intelligent Recommendation Method for Service Personalized Customization." IFAC-PapersOnLine 52, no. 13 (2019): 1543–48. http://dx.doi.org/10.1016/j.ifacol.2019.11.419.
Full text
41
Wang, Pu. "A Collaborative Filtering Recommendation Algorithm Based on Product Clustering." Applied Mechanics and Materials 267 (December 2012): 87–90. http://dx.doi.org/10.4028/www.scientific.net/amm.267.87.
Full textAbstract:
E-commerce recommendation system is one of the most important and the most successful application field of information intelligent technology. Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the customers. Recommendation algorithm is the core of the recommendation system. Collaborative filtering recommendation algorithm is the personalized recommendation algorithm that is used widely in e-commerce recommendation system. Collaborative filtering has been a comprehensive approach in recommendation system. But data are always sparse. This becomes the bottleneck of collaborative filtering. Collaborative filtering is regarded as one of the most successful recommender systems within the last decade, which predicts unknown ratings by analyzing the known ratings. In this paper, an electronic commerce collaborative filtering recommendation algorithm based on product clustering is given. In this approach, the clustering of product is used to search the recommendation neighbors in the clustering centers.
42
Beem, Varun Reddy. "AI-Driven Personalization in Retail: Transforming Customer Experience Through Intelligent Product Recommendations." European Journal of Computer Science and Information Technology 13, no. 38 (2025): 117–31. https://doi.org/10.37745/ejcsit.2013/vol13n38117131.
Full textAbstract:
This technical article explores the transformative impact of artificial intelligence on retail personalization, focusing on how advanced AI solutions like Amazon Personalize and fine-tuned language models are revolutionizing product recommendations and customer engagement. It examines a case study of an online fashion retailer that implemented a hybrid personalization system, combining recommendation algorithms with generative AI for dynamic content creation. The multi-layered architecture captures subtle behavioral signals, processes them through sophisticated recommendation engines, and delivers contextually relevant product suggestions with personalized descriptions. The article analyzes the significant business outcomes achieved through this implementation and details the technical considerations that organizations must address when building similar systems, including data pipeline architecture, model training strategies, privacy controls, and experimentation frameworks. The article concludes by exploring emerging frontiers in retail personalization technology, including multimodal recommendation systems that integrate visual and textual data, emotion-aware personalization that adapts to customer mood, and cross-channel personalization that creates consistent experiences across all touchpoints.
43
Yang, Ning. "Optimization of Personalized English Learning Paths through Mobile Interaction Technology." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 05 (2025): 195–209. https://doi.org/10.3991/ijim.v19i05.54527.
Full textAbstract:
With the development of information technology (IT), particularly the widespread application of mobile internet and smart devices, traditional methods of English language learning can no longer meet the personalized needs of modern learners. The design and recommendation of personalized learning paths have become key issues in enhancing learning outcomes. Current study primarily focuses on personalized recommendation systems based on big data and artificial intelligence (AI) algorithms. While these systems have achieved a certain degree of accuracy in recommending learners’ interests and learning content, problems such as recommendation precision, dynamic adaptation to changing interests, and insufficient integration of diversified learning scenarios persist. Therefore, improving the adaptability of personalized learning systems through more intelligent and dynamic learning path optimization methods remains a pressing challenge in this field. Building on existing research, a personalized English learning interest point recommendation model based on the graph convolutional network (GCN) was proposed, and personalized learning paths were optimized by incorporating multidimensional contextual information. The GCN was used to uncover the relationships between learners and knowledge points, thus constructing a precise interest point recommendation mechanism. Additionally, learning paths were dynamically adjusted by considering learners’ historical behaviors, learning progress, and situational context, offering a personalized learning experience. This study advances the development of personalized learning recommendation technologies and provides English learners with a more intelligent and precise learning path optimization solution.
44
Duan, Chao, Jin Yang, Qiaoling Cui, Wenlong Zhang, Xuelian Wan, and Mingyan Zhang. "Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph." Applied Sciences 15, no. 8 (2025): 4204. https://doi.org/10.3390/app15084204.
Full textAbstract:
Personalized learning resource recommendation is an essential component of intelligent tutoring systems. To address the issue of the plethora of learning resources and enhance the learner experience in intelligent tutoring systems, learning resource recommendation systems have been developed to model learners’ preferences. Despite numerous efforts and achievements in academia and industry toward more personalized learning, intelligent education tailored to individual learners still faces challenges, such as inadequate user representation and potential information loss during the aggregation of multi-source heterogeneous information features. In recent years, knowledge-graph-based recommendation systems have brought hope for mitigating these issues and achieving more accurate recommendations. In this paper, we propose a novel personalized learning resource recommendation method based on a knowledge graph named the Learner-Enhanced Knowledge Graph Attention (LKGA) network. This model enhances learner representation by extracting collaborative signals, where learning resources clicked by learners who have clicked the same resource are considered potential collaborative signals and are concatenated with the original learning resource features to form the initial entity set for the learner. Furthermore, during the entity aggregation process, each tail entity has different semantic expressions, and an attention mechanism is used to distinguish the importance of different neighbor entities. Additionally, residual connections are added in each hop of the learner’s aggregation process, with the information from the first hop added to each subsequent hop to reduce information loss. We applied the proposed LKGA model to a real-world dataset, and the experimental results fully validate the effectiveness of our model.
45
Tallari, Ratnamala. "Devspace: A Social Hub for Developers to Connect, Share and Grow Professionally Using Generative AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50380.
Full textAbstract:
Abstract - DevSpace is a next-generation social platform engineered to foster collaboration, networking, and professional growth among developers. Leveraging cutting-edge Generative AI technologies, particularly Retrieval-Augmented Generation (RAG) and Transformer-based models, DevSpace delivers real-time code recommendations, AI-driven discussions, and intelligent networking. This paper presents an ensemble AI approach to optimize developer interactions on the platform through context-aware content generation, recommendation systems, and adaptive learning models. DevSpace not only enhances productivity and knowledge sharing but also encourages continuous learning by providing dynamic tools such as AI-generated tutorials, debugging support, and project ideation tailored to user profiles. Key Words: Retrieval-Augmented Generation (RAG), Generative AI, Developer Collaboration, Transformer Models, AI Recommendation Engine, Social Coding Platform, Context-Aware Systems, Ensemble Learning, Personalized Content, Intelligent Networking
46
Wang, Winston. "Personalized medical recommendation system supported by medical data." Applied and Computational Engineering 45, no. 1 (2024): 40–46. http://dx.doi.org/10.54254/2755-2721/45/20241024.
Full textAbstract:
A personalized medical recommendation system is an intelligent system that utilizes medical data to provide targeted medical advice and services to individuals. With the lack of accumulation and development of medical data, personalized medical recommendation systems have great potential in improving medical effectiveness and saving medical resources. This article aims to explore the principles, methods, and applications of personalized medical recommendation systems based on medical data.
47
Chen, Jiyue. "The Investigation on Anime-Themed Recommendation Systems." Highlights in Science, Engineering and Technology 81 (January 26, 2024): 121–31. http://dx.doi.org/10.54097/36drh331.
Full textAbstract:
In today's digital era, the way we enjoy entertainment has experienced a significant revolution, and this transformation also encompasses the world of anime. With a seemingly endless array of anime series accessible through online streaming services, users frequently grapple with a formidable dilemma: the paradox of abundance. This paper introduces a sophisticated anime recommendation system, carefully crafted to tackle this quandary and enhance the overall anime-watching journey. Drawing upon the capabilities of machine learning and data analysis, the system's primary goal is to provide personalized anime recommendations based on user preferences and behavior. This paper not only outlines the development of an intelligent anime recommendation system but also takes a critical look at the existing body of research in the fields of recommendation systems and anime-related studies. Emphasizing the significance of personalized recommendations, it highlights the crucial role they play in enhancing user engagement and satisfaction within the world of anime streaming. The system itself is a culmination of various techniques and methodologies, employing a hybrid approach that combines collaborative filtering, content-based filtering, and advanced machine learning techniques. Linear models, random forests, and boosting algorithms are skillfully harnessed for prediction purposes, showcasing the system's versatility and adaptability. Preliminary results presented in this paper offer a tantalizing glimpse into the system's potential to deliver tailored recommendations, ultimately enriching the user experience and fostering greater engagement with the captivating universe of anime.
48
Sinha, Abhinav. "Style Craft: AI-Driven Fashion Platform." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47571.
Full textAbstract:
1. ABSTRACT As artificial intelligence continues to reshape industries, personalized and intelligent systems are becoming essential for enriching digital experiences. Style Craft: AI-Driven Fashion Platform introduces a next-generation fashion assistant designed to redefine how users discover, interact with, and personalize their style choices. The platform delivers curated fashion recommendations, enables virtual outfit trials, and helps users stay updated with current trends through an intuitive and immersive interface. Built with Python and enhanced by cutting-edge AI methodologies, the system leverages computer vision, natural language understanding, and recommendation engines to offer dynamic suggestions tailored to individual preferences, body profiles, and browsing behavior. Core components include an AI-powered virtual try-on system, style compatibility analysis, and trend forecasting modules, all accessible through a responsive web interface. This paper details the system's architecture and the technologies that power it, emphasizing how AI elevates personalization, visual recognition, and interaction design in the fashion domain. It also addresses implementation challenges, including optimizing garment recognition, adapting to user variability, and maintaining fluid performance. Looking forward, the platform envisions broader capabilities such as conversational AI for voice-guided fashion navigation, AR/VR support for immersive try-ons, and integration with real-time retail inventories for seamless shopping. Style Craft underscores the innovative potential of AI in crafting tailored, engaging, and futuristic fashion experiences for modern users. ACM Reference Format: Mitansh Sehgal, Nikhil Maurya, Abhinav Sinha. 2025. Style Craft: AI-Driven Fashion Platform. Keywords – Artificial Intelligence, Personalized Recommendations, Fashion Technology, , Trend Forecasting, Recommendation Systems, User Personalization, Human-Computer Interaction, Conversational AI, Style Analysis, Intelligent Fashion Assistant, E-commerce Innovation
49
Xin, Xin, Tianlei Shi, and Mishal Sohail. "Knowledge-Based Intelligent Education Recommendation System with IoT Networks." Security and Communication Networks 2022 (March 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/4140774.
Full textAbstract:
The intelligent education recommendation system can recommend knowledge suitable for students' personal learning. However, the traditional recommendation algorithm has generality problems, which lead to poor knowledge recommendation effects. In order to improve the performance of the education recommendation system, based on the machine learning algorithm, this paper combines the knowledge graph algorithm to improve the recommendation algorithm and decomposes the matrix with a higher dimension into several matrices with relatively small dimensions through matrix transformation. Moreover, this paper conducts in-depth mining of the potential attributes of users and items and improves the matrix decomposition formula based on knowledge recommendation requirements. In addition, this paper constructs the framework of the intelligent education recommendation system with IoT networks based on the analysis of functional requirements. Finally, this paper designs experiments to verify and analyze the model from the perspective of model performance and user satisfaction. The research results show that the algorithm model constructed in this paper is effective.
50
Rani, Geeta, Vijaypal Singh Dhaka, Sonam, Upasana Pandey, and Pradeep Kumar Tiwari. "Intelligent and Adaptive Web Page Recommender System." International Journal of Web Services Research 18, no. 4 (2021): 27–50. http://dx.doi.org/10.4018/ijwsr.2021100102.
Full textAbstract:
In this manuscript, an intelligent and adaptive web page recommender system is proposed that provides personalized, global, and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: uniformity and recommendation strength. The system continuously tracks the user's responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset, which is a significant improvement over the 70% F1 measure reported by automatic clustering-based genetic algorithm, the prior web recommender system.