Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering

• Published in: IEEE transactions on cybernetics Vol. 49; no. 7; pp. 2678 - 2692
• Main Authors: Wang, Chang-Dong, Deng, Zhi-Hong, Lai, Jian-Huang, Yu, Philip S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cold; Cold items; Collaboration; collaborative filtering (CF); Electronic commerce; Filtering algorithms; Filtration; Food service; innovators; Internet; Real-time systems; recommender system; Recommender systems; serendipity; Sorting; Technological innovation; User behavior
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2018.2841924

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 innovatorbased 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.