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Journal articles on the topic 'Recommendation System; Cold Start Problem; Data Sparsity Problem'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Darapisut, Sumet, Komate Amphawan, Nutthanon Leelathakul, and Sunisa Rimcharoen. "A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors." ISPRS International Journal of Geo-Information 12, no. 10 (2023): 431. http://dx.doi.org/10.3390/ijgi12100431.

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Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors

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Li, Zhixian. "Exploring the Path of Innovative Development of Traditional Culture under Big Data." Computational Intelligence and Neuroscience 2022 (August 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/7715851.

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Chinese traditional culture is the treasure of our cultural field. In the new era, it is of great significance to give traditional culture a new life and vitality. The term “big data” is hotly debated all over the world, while the development of big data is gradually occupying all aspects of the society that people are compatible with society. It is an imperative initiative to build a cultural data system by making use of big data technology, and cultural big data can make Chinese traditional culture release more vitality. This paper analyzes the new characteristics of traditional culture deve

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Li, Yikai, Wentai Li, Weiran Su, Yingkai Tang, and Jingyu Miao. "Attempted Improvements on Existing Recommendation System." Applied and Computational Engineering 132, no. 1 (2025): 148–58. https://doi.org/10.54254/2755-2721/2024.20584.

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This paper presents an exploration and enhancement of existing recommendation systems by addressing key challenges such as the cold-start problem and data sparsity. We propose DGSR++, a hybrid model that combines dynamic user-item interaction data with content-based features to enhance recommendation quality. Additionally, we develop MPKG lite, a lightweight knowledge graph embedding model, to mitigate cold-start issues. Besides, we also improved other models, such as GTN and ensembled learning. The paper also evaluates the performance of ensemble learning methods. Experimental results demonst

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Li, Zehang. "NCF-based Movie Recommendation System." Applied and Computational Engineering 104, no. 1 (2024): 72–77. http://dx.doi.org/10.54254/2755-2721/104/20241166.

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Abstract. This paper discusses the design and evaluation of a Neural Collaborative Filtering (NCF) model for movie recommendations using the MovieLens dataset. It addresses the limitations of traditional recommendation systems, such as content-based filtering and collaborative filtering, which struggle with data sparsity and the cold start problem. By incorporating deep learning, the NCF model enhances the accuracy and personalization of recommendations by learning the latent features of users and items and capturing complex interactions.

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Yuan, Jinfeng, and Li Li. "Recommendation Based on Trust Diffusion Model." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/159594.

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Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship

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Li, Lianhuan, Zheng Zhang, and Shaoda Zhang. "Hybrid Algorithm Based on Content and Collaborative Filtering in Recommendation System Optimization and Simulation." Scientific Programming 2021 (May 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/7427409.

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This paper explores and studies recommendation technologies based on content filtering and user collaborative filtering and proposes a hybrid recommendation algorithm based on content and user collaborative filtering. This method not only makes use of the advantages of content filtering but also can carry out similarity matching filtering for all items, especially when the items are not evaluated by any user, which can be filtered out and recommended to users, thus avoiding the problem of early level. At the same time, this method also takes advantage of the advantages of collaborative filteri

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Zhang, En, Wenming Ma, Jinkai Zhang, and Xuchen Xia. "A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning." Electronics 12, no. 24 (2023): 5034. http://dx.doi.org/10.3390/electronics12245034.

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Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, th

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Mustapha, Maidawa, Y. Dutse A., Ahmad Aminu, Ya'u Gital Abdulsalam, and Zahraddeen Yakubu Ismail. "An Improvised Business Intelligence Recommender System using Data Mining Algorithm." An Improvised Business Intelligence Recommender System using Data Mining Algorithm 8, no. 11 (2023): 12. https://doi.org/10.5281/zenodo.10297550.

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AI allows for a higher quality of recommendation than can be achieved by conventional recommendation methods. This has ushered in a new era for recommender systems, creating advanced observations of the relationship between users and items, presented an expanded understanding of demographic, textural, virtual, and contextual data as well as more intricate data representations. However, the challenge for the recommendation systems is to solve the problems of sparsity, scalability, and cold start. The existing capsule networks take times in training making it a slow algorithm. Also, ignoring the

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Nasy`an Taufiq Al Ghifari, Benhard Sitohang, and Gusti Ayu Putri Saptawati. "Addressing Cold Start New User in Recommender System Based on Hybrid Approach: A review and bibliometric analysis." IT Journal Research and Development 6, no. 1 (2021): 1–16. http://dx.doi.org/10.25299/itjrd.2021.vol6(1).6118.

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Increasing number of internet users today, the use of e-commerce becomes a very vital need. One of the keys that holds the success of the e-commerce system is the recommendation system. Collaborative filtering is the popular method of recommendation system. However, collaborative filtering still has issues including data sparsity, cold start, gray sheep, and dynamic taste. Some studies try to solve the issue with hybrid methods that use a combination of several techniques. One of the studies tried to solve the problem by building 7 blocks of hybrid techniques with various approaches. However,

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Cheng, Jiujun, Yingbo Liu, Huiting Zhang, Xiao Wu, and Fuzhen Chen. "A New Recommendation Algorithm Based on User’s Dynamic Information in Complex Social Network." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/281629.

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The development of recommendation system comes with the research of data sparsity, cold start, scalability, and privacy protection problems. Even though many papers proposed different improved recommendation algorithms to solve those problems, there is still plenty of room for improvement. In the complex social network, we can take full advantage of dynamic information such as user’s hobby, social relationship, and historical log to improve the performance of recommendation system. In this paper, we proposed a new recommendation algorithm which is based on social user’s dynamic information to

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Gao, Ruiyan. "Exploring the landscape of recommendation systems: A qualitative analysis of types, challenges, and potential solutions." Applied and Computational Engineering 64, no. 1 (2024): 217–22. http://dx.doi.org/10.54254/2755-2721/64/20241444.

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With the rapid development of computers and the Internet, recommendation systems have become an integral part of our daily lives, providing personalized suggestions for movies, products, etc. The aim of this paper is to summarize the types of recommendation systems, which are mainly classified into content-based recommendation systems and collaborative filtering-based recommendation systems. Content-based recommendation systems recommend products based on user interests and product descriptions, while collaborative filtering systems recommend products based on similarities between users or pro

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Lubis, Ali Akbar, Ronsen Purba, Megawaty Simamora, and Anna Agustiana. "Uji Akurasi Algoritma Bipolar Slope One dan BW-Mine pada Sistem Rekomendasi." Jurnal SIFO Mikroskil 20, no. 1 (2019): 51–58. http://dx.doi.org/10.55601/jsm.v20i1.646.

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The recommendation system is widely applied to various e-commerce. There are some problems that can cause the recommendation system to fail. This problem is about the massive vacuum of rating data (sparsity) and cold start. Therefore, the right recommendation method is needed to improve accuracy, so that the user can find the item according to desire.To achieve this goal, bipolar slope one is used to predict the rating. Bipolar slope one is used to predict the rating of an item. In predicting an item's rating, an item pattern is needed. This item pattern can be represented in the Assosiation R

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Di, Yicheng, and Yuan Liu. "MFPCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation." Applied Sciences 13, no. 7 (2023): 4407. http://dx.doi.org/10.3390/app13074407.

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Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches frequently require the centralized storage of user data, which poses a substantial risk of privacy breaches. Compared to traditional recommendation systems with centralized data, federated recommendation systems with multiple clients trained collaboratively have significant privacy benefits in terms of user data. While users’ interests are often p

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Wang, Xibin, Yunji Li, Jing Chen, and Jianfeng Yang. "Enhancing Personalized Recommendation by Transductive Support Vector Machine and Active Learning." Security and Communication Networks 2022 (March 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/1705527.

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As an important component of information service networks, personalized recommendation technology provides users with better options and enables them to obtain information anytime and anywhere. Collaborative filtering (CF) is a successful and widely used form of this technology. However, the traditional CF recommendation algorithm is ineffective in environments with frequent entry of new users and high levels of data sparsity. For new users in the system, few or no scores, labels, or other such information is available, leading to the user cold start problem. Simultaneously, data sparsity lead

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Bao, Dianqing, and Wen Su. "Personalized Intelligent Recommendation Model Construction Based on Online Learning Behavior Features and CNN." Information Technology and Control 53, no. 1 (2024): 115–27. http://dx.doi.org/10.5755/j01.itc.53.1.34317.

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The current intelligent recommendation models in online learning systems suffer from data sparsity and cold start problems. To address the data sparsity problem, a collaborative filtering recommendation algorithm model (SACM-CF) based on an automatic coding machine is proposed in the study. The model can extract the online learning behavior features of users and match these features with the learning resource features to improve the recommendation precision. For the cold-start problem, the study proposes a CBCNN model based on CNN, using the language model as the input of the model and the imp

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Hema Sri, Podakanti. "Real-Time Recommendations for E-Commerce." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49375.

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ABSTRACT A recommendation system for e-commerce products utilizing collaborative filtering approaches aims to personalize the online shopping experience by analyzing user behavior and preferences. Collaborative filtering operates on the principle that users with similar interests will prefer similar items. There are two primary types: memory-based, which includes user-based and item-based filtering relying on historical user-item interactions, and model-based, which employs techniques like matrix factorization (e.g., Singular Value Decomposition) to uncover latent factors influencing user pref

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Lu, Qibei, Feipeng Guo, Wei Zhou, Zifan Wang, and Shaobo Ji. "Mobile Social Recommendation Model Integrating Users’ Personality Traits and Relationship Strength under Privacy Concerns." Systems 10, no. 6 (2022): 198. http://dx.doi.org/10.3390/systems10060198.

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Aiming at the problem of data sparsity, cold start, and privacy concerns in complex information recommendation systems, such as personalized marketing on Alibaba or TikTok, this paper proposes a mobile social recommendation model integrating users’ personality traits and social relationship strength under privacy concerns (PC-MSPR). Firstly, PC-MSPR focuses on specific personality traits, including openness, extraversion, and agreeableness, and their impacts on mobile users’ online behaviors. A personality traits calculation method that incorporates privacy preferences (PP-PTM) is then introdu

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Ajay, Agarwal*1 &. Minakshi2. "EDUCATIONAL DATA SETS AND TECHNIQUES OF RECOMMENDER SYSTEMS: A SURVEY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 10 (2017): 434–43. https://doi.org/10.5281/zenodo.1036288.

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Due to growth of World Wide Web, enormous data are created. To get the information out of available data it is necessary to store these data in a particular format. These formatted data are called datasets. These datasets are important for extracting information in such a way so that decision can be taken to recommend the trend embedded in the datasets. In addition, they can be used to test and train many information processing applications. A general practice to use available datasets obtained from different application environments is to evaluate developed recommendation techniques. Such tec

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Mini, T. V. "Recommender Systems: Enhancing Prediction Accuracy Through Hybrid Data Mining Techniques." International Journal of Information Technology Research Studies (IJITRS) 1, no. 1 (2025): 7–19. https://doi.org/10.5281/zenodo.15309672.

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This research explores the integration of multiple data mining approaches to improve recommendation accuracy in modern recommender systems. Despite significant advancements in recommendation algorithms, challenges persist in addressing the cold-start problem, data sparsity, and preference volatility. This study investigates how hybrid techniques combining collaborative filtering, content-based filtering, and knowledge-based approaches can overcome these limitations. Using a comprehensive dataset from an e-commerce platform with 2.3 million user-item interactions, we implemented a novel hybrid

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Neelima Jain and Dr. Abid Hussain. "A Hybrid Machine Learning Approach for Improving E-Commerce Recommendation Systems Using Python." International Journal of Scientific Research in Science and Technology 11, no. 6 (2024): 1092–105. https://doi.org/10.32628/ijserset242436.

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This paper presents a novel hybrid recommendation system approach that combines collaborative filtering, content-based filtering, and deep learning techniques to improve recommendation accuracy and overcome common challenges in e-commerce platforms. Our proposed model addresses key limitations such as the cold-start problem, data sparsity, and overspecialization by leveraging the complementary strengths of multiple recommendation strategies. Implementation using Python demonstrates significant performance improvements across various evaluation metrics compared to standalone methods, providing

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Neelima Jain and Dr. Abid Hussain. "A Hybrid Machine Learning Approach for Improving E-Commerce Recommendation Systems Using Python." International Journal of Scientific Research in Science and Technology 11, no. 6 (2024): 1092–105. https://doi.org/10.32628/ijsrst251263.

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This paper presents a novel hybrid recommendation system approach that combines collaborative filtering, content-based filtering, and deep learning techniques to improve recommendation accuracy and overcome common challenges in e-commerce platforms. Our proposed model addresses key limitations such as the cold-start problem, data sparsity, and overspecialization by leveraging the complementary strengths of multiple recommendation strategies. Implementation using Python demonstrates significant performance improvements across various evaluation metrics compared to standalone methods, providing

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Hazar, Manar Joundy, Salah Zrigui, Mohsen Maraoui, Mounir Zrigui, and Henri Nicolas. "A Recommendation System Involving a Hybrid Approach of Student Review and Rating for an Educational Video." Journal of the Brazilian Computer Society 29, no. 1 (2023): 73–85. http://dx.doi.org/10.5753/jbcs.2023.3063.

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Video recommendation systems in e-learning platforms are a specific type of recommendation system that uses algorithms to suggest educational videos to students based on their interests and preferences. Student’s written feedback or reviews can provide more details about the educational video, including its strengths and weaknesses. In this paper, we build an education video recommender system based on learners’ reviews. we use LDA topic model on textual data extracted from educational videos to train language models as an input to supervised CNN model. Additionally, we used latent factor mode

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Shukla,, Arpit. "Book Recommendation System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33803.

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In the digital age, the vast availability of books poses a significant challenge for readers to discover titles that match their interests and preferences. This report explores the use of machine learning for a Book Recommendation System (BRS) to help readers discover books suited to their interests in the digital age. It will utilize advanced algorithms and techniques, including collaborative filtering, content-based filtering, and hybrid models, to offer personalized recommendations based on users' reading history and behavior. The project aims to tackle challenges like data sparsity and the

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Alrashidi, Muhammad, Roliana Ibrahim, and Ali Selamat. "Hybrid CNN-based Recommendation System." Baghdad Science Journal 21, no. 2(SI) (2024): 0592. http://dx.doi.org/10.21123/bsj.2024.9756.

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Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model i

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Khalid, Asra, Karsten Lundqvist, Anne Yates, and Mustansar Ali Ghzanfar. "Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)." PLOS ONE 16, no. 1 (2021): e0245485. http://dx.doi.org/10.1371/journal.pone.0245485.

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Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermo

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Sagedur Rahman. "Extended Collaborative Filtering Recommendation System with Adaptive KNN and SVD." International Journal of Engineering and Management Research 13, no. 4 (2023): 105–12. http://dx.doi.org/10.31033/ijemr.13.4.14.

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In recent years, recommendation systems have gained significant importance due to the vast amount of digital content available on various online platforms. Collaborative filtering is a widely adopted approach in recommendation systems, leveraging user-item interactions to make personalized predictions. However, traditional collaborative filtering methods face challenges such as the cold-start problem and data sparsity. To address these issues, researchers have proposed advanced techniques, including Adaptive KNN-Based and SVD-Based Extended Collaborative Filtering. This paper provides a compre

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Zhang, Xinyi. "Research on Intelligent Recommendation Algorithm Based on Deep Learning." International Journal of Computer Science and Information Technology 4, no. 3 (2024): 299–309. https://doi.org/10.62051/ijcsit.v4n3.31.

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With the wide application of intelligent recommendation system in e-commerce shopping, video websites and social media platforms, timeliness, accuracy, scalability and interpretability have gradually become important criteria to measure the excellence of a recommendation system.The most widely used recommendation system is collaborative filtering recommendation system. Its advantages include high accuracy, good real-time performance, and strong scalability, but there are still disadvantages, including cold start, data sparsity, and vulnerability. In the current flourishing of deep learning res

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Hu, Liang, Songlei Jian, Longbing Cao, Zhiping Gu, Qingkui Chen, and Artak Amirbekyan. "HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3830–37. http://dx.doi.org/10.1609/aaai.v33i01.33013830.

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Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To th

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Zhang, Suzhi, Zijian Bai, Pu Li, and Yuanyuan Chang. "Multi-Graph Convolutional Network for Fine-Grained and Personalized POI Recommendation." Electronics 11, no. 18 (2022): 2966. http://dx.doi.org/10.3390/electronics11182966.

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With the advent of the era of rapid information expansion, the massive data backlog that exists on the Internet has led to a serious information overload problem, which makes recommendation systems a crucial part of human life. In particular, the Point-Of-Interest (POI) recommendation system has been applied to many real-life scenarios, such as life services and autonomous driving. Specifically, the goal of POI recommendation is to recommend locations that match their personalized preferences to users. In existing POI recommendation methods, people tend to pay more attention to the impact of t

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Huang, Weiming, Baisong Liu, and Zhaoliang Wang. "Paper Recommendation via Correlation Pattern Mining and Attention Mechanism." Journal of Sensors 2023 (October 18, 2023): 1–14. http://dx.doi.org/10.1155/2023/3311363.

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In this paper, we improve the efficiency and effectiveness of the matrix factorization method in the paper recommendation system. We mainly address two problems. First, the vectors based on citation networks are undertrained because newly added papers are rarely cited. Second, current algorithms are mainly based on keyword search or global popularity and lack the organic combination of considering personalized interest and global popularity. To address the above two issues for the paper recommender, we propose a matrix factorization model that combines popularity analysis and attention mechani

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Qiu, Gang, Changjun Song, Liping Jiang, and Yanli Guo. "Multi-view hybrid recommendation model based on deep learning." Intelligent Data Analysis 26, no. 4 (2022): 977–92. http://dx.doi.org/10.3233/ida-215988.

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With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, the scale of data continues to grow. The recommendation system has become one of the important intelligent software to help users make decisions. The recommendation model based on user rating data is widely studied and applied, but the data sparsity problem and the cold start problem seriously affect the recommendation quality. In this paper, Multi-view Hybrid Recommendation Model (MHRM) based on deep learning is proposed. First, we use WLDA (an improved Latent Dirichlet Allocation method)

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Jia, Yunjie. "Exploratory Research on the Practice of College English Classroom Teaching Based on Internet and Artificial Intelligence." Security and Communication Networks 2022 (July 12, 2022): 1–9. http://dx.doi.org/10.1155/2022/7133654.

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With the rapid development of cloud computing and mobile Internet technology, the information of optional services in the network is exploding and the problem of information overload is becoming more and more serious. The recommendation system can recommend suitable English online classes for students according to their interests and needs, effectively reduce the data load, help students extract effective information from the mass of information, and make accurate recommendations. Aiming at the problems of data sparsity and cold start in the recommendation system, this paper proposes a recomme

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Patel, Mr Tirth, Mr Manan Bhayani, Mr Kishan Kayadra, and Ms Manisha Vasava. "Movies Recommendation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44190.

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This paper presents a comprehensive review of movie recommendation systems and their applications in the entertainment industry. The paper highlights the different techniques used in movie recommendation systems such as collaborative filtering, content-based filtering, and hybrid filtering. The strengths and weaknesses of each technique are discussed, along with their respective algorithms. The paper also explores the challenges faced by movie recommendation systems such as data sparsity, cold start problem, and user bias. Furthermore, the paper examines the current state-of-the-art movie reco

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Ojagh, Soroush, Mohammad Reza Malek, and Sara Saeedi. "A Social–Aware Recommender System Based on User’s Personal Smart Devices." ISPRS International Journal of Geo-Information 9, no. 9 (2020): 519. http://dx.doi.org/10.3390/ijgi9090519.

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Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE

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Yang, Qing, Peiling Yuan, and Xi Zhu. "Research of Personalized Course Recommended Algorithm based on the Hybrid Recommendation." MATEC Web of Conferences 173 (2018): 03067. http://dx.doi.org/10.1051/matecconf/201817303067.

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This paper presents a personalized course recommended algorithm based on the hybrid recommendation. The recommendation algorithm uses the improved NewApriori algorithm to implements the association rule recommendation, and the user-based collaborative filtering algorithm is the main part of the algorithm. The hybrid algorithm adds the weight to the recommendation result of the user-based collaborative filtering and association rule recommendation, implementing a hybrid recommendation algorithm based on both of them. It has solved the problem of data sparsity and cold-start partially and provid

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Hriekes, EEva Diab, and Yosser AlSayed Souleiman AlAtassi. "Improve the Performance of Advice Systems Based on Cooperative Liquidation Using Trust Relationships." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (2019): 87–106. http://dx.doi.org/10.29196/jubpas.v27i1.2068.

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Recommender systems are one of the recent inventions to deal with information overload problem and provide users with personalized recommendations that may be of their interests. Collaborative filtering is the most popular and widely used technique to build recommender systems and has been successfully employed in many applications. However, collaborative filtering suffers from several inherent issues that affect the recommendation accuracy such as: data sparsity and cold start problems caused by the lack of user ratings, so the recommendation results are often unsatisfactory. To address these

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Zhang, Zhuohao, Jinghua Zhu, and Chenbo Yue. "Session-Based Graph Attention POI Recommendation Network." Wireless Communications and Mobile Computing 2022 (July 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/6557936.

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Point-of-interest (POI) recommendation which aims at predicting the locations that users may be interested in has attracted wide attentions due to the development of Internet of Things and location-based services. Although collaborative filtering based methods and deep neural network have gain great success in POI recommendation, data sparsity and cold start problem still exist. To this end, this paper proposes session-based graph attention network (SGANet for short) for POI recommendation by making use of regional information. Specifically, we first extract users’ features from the regional h

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Samadhiya, Sadhna, and Cooper Cheng-Yuan Ku. "Hybrid Approach to Improve Recommendation of Cloud Services for Personalized QoS Requirements." Electronics 13, no. 7 (2024): 1386. http://dx.doi.org/10.3390/electronics13071386.

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Cloud-service recommendation systems make suggestions based on ratings provided by cloud users. These ratings may contain sparse data, which makes it difficult to speculate on suitable cloud services. Moreover, new cloud users often suffer from cold-start difficulties. Therefore, in this study, we attempt to better overcome these two challenges, i.e., cold start and data sparsity, using a hybrid approach incorporating neural matrix factorization, deep autoencoders, and suitable questionnaires. The proposed approach provides a list of the top N cloud service providers for old cloud users based

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Zeng, Xuhui, Shu Wang, Yunqiang Zhu, Mengfei Xu, and Zhiqiang Zou. "A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features." ISPRS International Journal of Geo-Information 11, no. 12 (2022): 625. http://dx.doi.org/10.3390/ijgi11120625.

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The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and ‘cold start’, since they do not consider the complex geographical features. To address the above issu

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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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Enas M. Turki. "Enhancing E-Commerce Recommendations Through Data-Driven Approaches: A Case Study of Amazon Product Reviews." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 269–79. https://doi.org/10.52783/jisem.v10i8s.1025.

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In the dynamic landscape of e-commerce, personalized product recommendation systems are pivotal in enhancing user experiences and driving business success. This study leverages the Amazon Product Reviews dataset, a rich source of user-generated feedback, to design a scalable and effective personalized recommendation system. Adopting a structured methodology encompassing four data analysis phases includes descriptive, diagnostic, predictive, and prescriptive. This research extracts meaningful insights from product reviews and ratings. The study captures user preferences and sentiments using adv

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Xu, Haoyu, Guodong Wu, Enting Zhai, Xiu Jin, and Lijing Tu. "Preference-Aware Light Graph Convolution Network for Social Recommendation." Electronics 12, no. 11 (2023): 2397. http://dx.doi.org/10.3390/electronics12112397.

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Social recommendation systems leverage the abundant social information of users existing in the current Internet to mitigate the problem of data sparsity, ultimately enhancing recommendation performance. However, most existing recommendation systems that introduce social information ignore the negative messages passed by high-order neighbor nodes and aggregate messages without filtering, which results in a decline in the performance of the recommendation system. Considering this problem, we propose a novel social recommendation model based on graph neural networks (GNNs) called the preference-

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Nijhawan, Laksh. "AI in Digital Entertainment: Exploring User-Centric Movie Prediction Systems." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48103.

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ABSTRACT This paper explores the development and implementation of a movie recommendation system powered by Artificial Intelligence (AI), focusing on the use of collaborative filtering techniques to enhance user experience in digital streaming platforms. By leveraging machine learning algorithms such as K-Nearest Neighbours (KNN) and Singular Value Decomposition (SVD), the system analyses user preferences and interactions to generate personalized movie recommendations. The backend of the system is built using Flask, while the frontend is developed with HTML, CSS, and JavaScript, ensuring an in

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Xie, Fang, Yiming Zhang, Krzysztof Przystupa, and Orest Kochan. "A Knowledge Graph Embedding Based Service Recommendation Method for Service-Based System Development." Electronics 12, no. 13 (2023): 2935. http://dx.doi.org/10.3390/electronics12132935.

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Web API is an efficient way for Service-based Software (SBS) development, and mashup is a key technology which merges several web services to deal with the increasing complexity of software requirements and expedite the service-based system development. The efficient service recommendation method is vital for the software development. However, the existing methods often suffer from data sparsity or cold start issues, which should lead to bad effects. Currently, this paper starts with SBS development, and proposes a service recommendation method based on knowledge graph embedding and collaborat

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Aji, Achmad Maezar Bayu, Dewi Nurdiyanti, and Hasan Basri. "Leveraging Neural Matrix Factorization (NeuralMF) and Graph Neural Networks (GNNs) for Enhanced Personalization in E-Learning Systems." International Journal Software Engineering and Computer Science (IJSECS) 4, no. 2 (2024): 463–72. http://dx.doi.org/10.35870/ijsecs.v4i2.2238.

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This study investigates the application of a combined approach utilizing Neural Matrix Factorization (NeuralMF++) and Graph Neural Networks (GNNs) to enhance personalization in e-learning recommendation systems. The primary objective is to address significant challenges commonly encountered in recommendation systems, such as data sparsity and the cold start problem, where new users or items need prior interaction history. NeuralMF++ leverages neural networks in matrix factorization to capture complex non-linear interactions between users and content. GNNs model intricate relationships between

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Bi, Zhongqin, Lina Jing, Meijing Shan, Shuming Dou, and Shiyang Wang. "Hierarchical Social Recommendation Model Based on a Graph Neural Network." Wireless Communications and Mobile Computing 2021 (August 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/9107718.

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With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method. Based on the theory of social relationship propagation, mining user relationships in social networks can alleviate the problems of data sparsity and the cold start of recommendation systems. Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation. The model is based on the graph neural network. It unifies the attention mechanism and bidirectional LSTM into the same

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Sisodiya, Mr Rishabh. "E-Commerce Recommendation System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30996.

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In today's digitally driven marketplace, E-commerce Product Recommendation Systems (EPRS) play a pivotal role in enhancing user experience, facilitating decision-making, and boosting sales. This paper offers a comprehensive analysis of various methodologies, techniques, and advancements in EPRS. Beginning with an overview of the significance of personalized recommendations in driving user engagement and satisfaction, the paper delves into the underlying principles and algorithms employed in EPRS, including collaborative filtering, content-based filtering, and hybrid approaches. Furthermore, th

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Wu, Jie. "Research on Product Design Strategy Based on User Preference and Machine Learning Intelligent Recommendation." Wireless Communications and Mobile Computing 2022 (April 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/7191410.

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In the machine learning model, intelligent recommendation system can select valuable information from a lot of data to help users find the products or services they need, which has been more and more widely used in recent years. However, there are still many problems in machine learning recommender systems, such as data sparsity, natural noise, and cold start, which leads to the fact that machine learning recommender systems cannot obtain accurate user preferences. When a project is rated, the quality of the recommendation is greatly affected. In order to solve the problem that the existing re

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Wu, Jun, Xinyu Song, Xiaxia Niu, et al. "IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust." Mathematics 10, no. 14 (2022): 2406. http://dx.doi.org/10.3390/math10142406.

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It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating th

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Sahoo, Sipra, and Bikram Kesari Ratha. "User profiling for web personalization using multi agent and DBSCAN based approach." International Journal of Engineering & Technology 7, no. 2 (2018): 849. http://dx.doi.org/10.14419/ijet.v7i2.10224.

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The user experience is enhanced by the Web Personalization System (WPS), which depends on the User's Interests (UI) and references are stored in the User Profile (UP). The profiles should be able to adapt and reproduce the change of user’s behavior for such system. Existing web page Recommendation Systems (RS) are still limited by several problems, some of which are the problem of recommending web pages to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and the problem of over-specialization. In this paper, the UI has been tracked and Dynamic

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