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Vasyl Lytvyn,Victoria Vysotska,Viktor Shatskykh,Ihor Kohut,Oksana Petruchenko,Lyudmyla Dzyubyk,Vitaliy Bobrivetc,Valentyna Panasyuk,Svitlana Sachenko,Myroslav Komar
Pág. 6 - 28
The paper reports a study into recommendation algorithms and determination of their advantages and disadvantages. The method for developing recommendations based on collaborative filtering such as Content-Based Filtering (CBF), Collaborative Filtering (C...
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Soroush Ojagh, Mohammad Reza Malek and Sara Saeedi
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 th...
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Sumet Darapisut, Komate Amphawan, Nutthanon Leelathakul and Sunisa Rimcharoen
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, ...
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FengLei Yang, Fei Liu and ShanShan Liu
Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative filtering research methods, but non-linear probabilistic models are beyond the limit of linear model capacity. For exa...
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Ninghua Sun, Tao Chen, Wenshan Guo and Longya Ran
The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborat...
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Jiagang Song, Jiayu Song, Xinpan Yuan, Xiao He and Xinghui Zhu
With the rapid development of Internet technology, how to mine and analyze massive amounts of network information to provide users with accurate and fast recommendation information has become a hot and difficult topic of joint research in industry and ac...
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Mingxuan Sun, Fei Li and Jian Zhang
Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with...
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Mingxuan Sun, Fei Li and Jian Zhang
Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with...
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Abdelghani Azri, Adil Haddi and Hakim Allali
Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user?item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in ...
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Christos Troussas, Akrivi Krouska, Antonios Koliarakis and Cleo Sgouropoulou
Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. ?his paper presents a novel approach to providing custom...
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Tae-Gyu Hwang and Sung Kwon Kim
A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF...
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Sun-Young Ihm, Shin-Eun Lee, Young-Ho Park, Aziz Nasridinov, Miyeon Kim and So-Hyun Park
Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user prefer...
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Bo Wang, Feiyue Ye and Jialu Xu
A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user ra...
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Bo Wang, Feiyue Ye and Jialu Xu
A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user ra...
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Hyeon Jo, Jong-hyun Hong and Joon Yeon Choeh
In recent years, virtual online communities have experienced rapid growth. These communities enable individuals to share and manage images or websites by employing tags. A collaborative tagging system (CTS) facilitates the process by which internet users...
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Can Cui, Jiwei Qin and Qiulin Ren
Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF method...
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Heba M. Wagih, Hoda M. O. Mokhtar and Samy S. Ghoniemy
Recently, social networks have shown huge potential in terms of collaborative web services and the study of peer influence as a result of the massive amount of data, datasets, and interrelations generated. These interrelations cannot guarantee the succes...
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Qingyao Ai, Vahid Azizi, Xu Chen and Yongfeng Zhang
Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms?especially the collaborative filtering (CF)- based approaches with shallow or deep models?usually work with various ...
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Hang Zhang, Mingxin Gan and Xi Sun
In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely ...
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Chunyang Liu, Jiping Liu, Shenghua Xu, Jian Wang, Chao Liu, Tianyang Chen and Tao Jiang
With the growing popularity of location-based social media applications, point-of-interest (POI) recommendation has become important in recent years. Several techniques, especially the collaborative filtering (CF), Markov chain (MC), and recurrent neural...
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