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Social Bayesian Personal Ranking for Missing Data in Implicit Feedback Recommendation

  • Yijia Zhang
  • Wanli Zuo
  • Zhenkun Shi
  • Lin Yue
  • Shining Liang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11061)

Abstract

Recommendation systems estimate user’s preference to suggest items that might be interesting for them. Recently, implicit feedback recommendation has been steadily receiving more attention because it can be collected on a larger scale with a much lower cost than explicit feedback. The typical methods for recommendation are not well-designed for implicit feedback recommendation. Some effective methods have been proposed to improve implicit feedback recommendation, but most of them suffer from the problems of data sparsity and usually ignore the missing data in implicit feedback. Recent studies illustrate that social information can help resolve these issues. Towards this end, we propose a joint factorization model under the BPR framework utilizing social information. Remarkable, the experimental results show that our method performs much better than the state-of-the-art approaches and is capable of solving implicit problems, which indicates the importance of incorporating social information in the recommendation process to address the poor prediction accuracy.

Keywords

Implicit feedback recommendation BPR Social information 

Notes

Acknowledgement

This work is sponsored by the Nature Science Foundation of Jilin Province (No. 20180101330JC), the National Nature Science Foundation of China (No. 60973040, No. 61602057), the Outstanding Young Talent Project of Jilin Providence (No. 2017052005954), the Fundamental Research Funds for the Central Universities (No. 2412017QD028), China Postdoctoral Science Foundation (No. 2017M621192), the Scientific and Technological Development Program of Jilin Province (No. 20180520022JH).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityJilinChina
  2. 2.Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of EducationChangchunChina
  3. 3.School of Computer Science and Information TechnologyNortheast Normal UniversityChangchunChina

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