A survey of collaborative filtering-based recommender systems for mobile internet applications

Yang, Zhe, Wu, Bing, Zheng, Kan, Wang, Xianbin, and Lei, Lei (2016) A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access, 4. pp. 3273-3287.

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With the rapid development and application of the mobile Internet, huge amounts of user data are generated and collected every day. How to take full advantages of these ubiquitous data is becoming the essential aspect of a recommender system. Collaborative filtering (CF) has been widely studied and utilized to predict the interests of mobile users and to make proper recommendations. In this paper, we first propose a framework of the CF recommender system based on various user data including user ratings and user behaviors. Key features of these two kinds of data are discussed. Moreover, several typical CF algorithms are classified as memory-based approaches and model-based approaches and compared. Two case studies are presented in an effort to validate the proposed framework.

Item ID: 54268
Item Type: Article (Research - C1)
ISSN: 2169-3536
Keywords: mobile internet, recommender system, collaborative filtering
Funders: National High-Tech R&D Program (NHTP), National Key Technology R&D Program of China (NKTP), China National Science Funding (CNSF), Fundamental Research Funds for the Central Universities (FRFCU)
Projects and Grants: NHTP grant 2015AA01A705, NKTP grant 2014ZX03003011-004, CNF grant 61271182, FRFCU grant 2014ZD03-02
Date Deposited: 28 Jun 2018 02:12
FoR Codes: 40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8901 Communication Networks and Services > 890103 Mobile Data Networks and Services @ 100%
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