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| Long Tail Recommendation Method Based on Social Network Information |
| FENG Chenjiao1, SONG Peng2, ZHANG Kaihan3, LIANG Jiye3 |
1. College of Applied Mathematics, Shanxi University of Finance and Economics, Taiyuan 030006;
2. School of Economics and Management, Shanxi University, Taiyuan 030006;
3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006 |
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Abstract In the long-tail recommendation scenario, target users are more likely to trust the recommendation results of the friends with similar interests. Therefore, recommending personalized preferences of friends to target users is conducive to improving the performance of long-tail recommendation methods. Accordingly, how to effectively fuse social network information with rating matrix information is an important issue in long-tail recommendation. In this paper, a long-tail recommendation method based on social network information is designed. From the perspective of information fusion, social network and rating matrix information are utilized to share potential feature vectors of users. The information of friend recommendation is taken as an important factor in the proposed recommendation model. User activity level, item unpopularity level, user-item preference level and friend recommendation behavior are taken as inputs, and variational inference is employed to get relevant unknown parameters to realize accurate prediction. Experiments show that the proposed method can recommend long-tail items effectively with high recommendation accuracy.
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Received: 08 June 2021
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| Fund:National Key Research and Development Program of China(No.2020AAA0106100), National Natural Science Foundation of China(No.72171137,61876103), Open Foundation of Key Laboratory of Shanxi Province(No.CICIP2020005) |
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Corresponding Authors:
SONG Peng, Ph.D., professor. His research interests include intelligent decision and data mining.
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About author:: FENG Chenjiao, Ph.D., lecturer. Her research interests include data mining and recommender system.
ZHANG Kaihan, Ph.D. candidate. Her research interests include data mining and machine learning.
LIANG Jiye, Ph.D., professor. His research interests include granular computing, data mining and machine learning. |
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2022, Vol. 35 
