Journal
IEEE ACCESS
Volume 4, Issue -, Pages 3273-3287Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2016.2573314
Keywords
Mobile Internet; recommender system; collaborative filtering
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Funding
- National High-Tech R&D Program (863 Program) [2015AA01A705]
- National Key Technology R&D Program of China [2014ZX03003011-004]
- China Natural Science Funding [61271183]
- Fundamental Research Funds for the Central Universities [2014ZD03-02]
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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.
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