Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 49, Issue 7, Pages 2678-2692Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2841924
Keywords
Cold items; collaborative filtering (CF); innovators; recommender system; serendipity
Categories
Funding
- National Key Research and Development Program of China [2016YFB1001003]
- NSFC [61502543, 61672313]
- Guangdong Natural Science Funds for Distinguished Young Scholar [2016A030306014]
- Tip-Top Scientific and Technical Innovative Youth Talents of Guangdong Special Support Program [2016TQ03X542]
- NSF [IIS-1526499, IIS-1763325, CNS-1626432]
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Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, which tends to recommend popular items and hence less popular items become increasingly less popular. Under this circumstance, most of the items in the recommendation list are already familiar to users and therefore the performance would seriously degenerate in finding cold items, i.e., new items and niche items. To address this issue, in this paper, a user survey is first conducted on the online shopping habits in China, based on which a novel recommendation algorithm termed innovator-based CF is proposed that can recommend cold items to users by introducing the concept of innovators. Specifically, innovators are a special subset of users who can discover cold items without the help of recommender system. Therefore, cold items can be captured in the recommendation list via innovators, achieving the balance between serendipity and accuracy. To confirm the effectiveness of our algorithm, extensive experiments are conducted on the dataset provided by Alibaba Group in Ali Mobile Recommendation Algorithm Competition, which is collected from the real e-commerce environment and covers massive user behavior log data.
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