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Dimitris Spiliotopoulos, Dionisis Margaris and Costas Vassilakis
One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the u...
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Dionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos and Stefanos Ougiaroglou
Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values tha...
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Dionisis Margaris, Dimitris Spiliotopoulos, Gregory Karagiorgos and Costas Vassilakis
Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near ...
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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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Songlin Tian, Ying Yang and Lei Yang
Business intelligence (BI), as a system for business data integration, processing, and analysis, is receiving increasing attention from enterprises. Data visualization is an important feature of BI, which allows users to visually observe the distribution...
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Ji-Yoon Kim and Chae-Kwan Lim
The electronic publication market is growing along with the electronic commerce market. Electronic publishing companies use recommendation systems to increase sales to recommend various services to consumers. However, due to data sparsity, the recommenda...
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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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Yuanyou Ou and Baoning Niu
The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problem...
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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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Bin Cheng, Ping Chen, Xin Zhang, Keyu Fang, Xiaoli Qin and Wei Liu
With the rapid development of ubiquitous data collection and data analysis, data privacy in a recommended system is facing more and more challenges. Differential privacy technology can provide strict privacy protection while reducing the risk of privacy ...
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Christie I. Ezeife and Hemni Karlapalepu
E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems? accuracy can be improved if complex sequential patterns of user purchase behavior ar...
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Luong Vuong Nguyen, Quoc-Trinh Vo and Tri-Hai Nguyen
In the current era of e-commerce, users are overwhelmed with countless products, making it difficult to find relevant items. Recommendation systems generate suggestions based on user preferences, to avoid information overload. Collaborative filtering is ...
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Triyanna Widiyaningtyas, Muhammad Iqbal Ardiansyah and Teguh Bharata Adji
One of the most prevalent recommendation systems is ranking-oriented collaborative filtering which employs ranking aggregation. The collaborative filtering study recently applied the ranking aggregation that considers the weight point of items to achieve...
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Ikram Karabila, Nossayba Darraz, Anas El-Ansari, Nabil Alami and Mostafa El Mallahi
Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these rating...
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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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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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Chenhong Luo, Yong Wang, Bo Li, Hanyang Liu, Pengyu Wang and Leo Yu Zhang
Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However,...
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Yunfei Zhang, Hongzhen Xu and Xiaojun Yu
An improved recommendation algorithm based on Conditional Variational Autoencoder (CVAE) and Constrained Probabilistic Matrix Factorization (CPMF) is proposed to address the issues of poor recommendation performance in traditional user-based collaborativ...
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Yong Zheng
Recommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situatio...
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Shini Renjith, A. Sreekumar , M. Jathavedan (Author)
Pág. e58653
Social media has significantly influenced modern lifestyle and the way in which most of the industries operate their business. Social media data refers to the contents created by users during their social interactions in the form of text, sound, visuals,...
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