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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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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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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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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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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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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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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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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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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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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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