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Artykuły w czasopismach na temat „Intelligent recommendation systems”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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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.

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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.

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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 thei

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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.

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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 system

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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.

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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

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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.

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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

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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.

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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.

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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. Th

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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.

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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, automate

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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.

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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 unsuit

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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.

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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

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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.

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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 protect

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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.

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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 mark

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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.

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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

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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.

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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

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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.

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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 impl

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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.

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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 p

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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.

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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 ha

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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.

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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

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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.

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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

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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.

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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.

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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,

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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.

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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, no

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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.

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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 highl

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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.

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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 p

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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.

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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.

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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.

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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 pla

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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.

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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 extrac

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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.

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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 collabor

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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.

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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 Processin

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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.

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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 tra

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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.

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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 monitori

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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.

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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 commun

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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.

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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 farmer

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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.

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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

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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.

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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.

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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 da

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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.

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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 deli

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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.

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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 learn

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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.

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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

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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.

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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.

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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.

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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 pr

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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.

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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 perso

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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.

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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 sys

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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.

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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

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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.

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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

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