To address the issue of data sparsity and cold-start in recommender system, social information (e.g., user-user trust links) has been introduced to complement rating data for improving the performances of traditional model-based recommendation techniques such as matrix factorization (MF) and Bayesian personalized ranking (BPR). Although effective, the utilization of the explicit user-user relationships extracted directly from such social information has three main limitations. First, it is difficult to obtain explicit and reliable social links. Only a small portion of users indicate explicitly their trusted friends in recommender systems. Second, the "cold-start" users are "cold" not only on rating but also on socializing. There is no significant amount of explicit social information that can be useful for "cold-start" users. Third, an active user can be socially connected with others who have different taste/preference. Direct usage of explicit social links may mislead recommendation. To address these issues, we propose to extract implicit and reliable social information from user feedbacks and identify top-k semantic friends for each user. We incorporate the top-k semantic friends information into MF and BPR frameworks to solve the problems of ratings prediction and items ranking, respectively. The experimental results on three real-world datasets show that our proposed approaches achieve better results than the state-of-the-art MF with explicit social links (with 3.0% improvement on RMSE), and social BPR (with 9.1% improvement on AUC).
|Original language||English (US)|
|Title of host publication||Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017|
|Editors||Nitesh Chawla, Wei Wang|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2017|
|Event||17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States|
Duration: Apr 27 2017 → Apr 29 2017
|Name||Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017|
|Conference||17th SIAM International Conference on Data Mining, SDM 2017|
|Period||04/27/17 → 04/29/17|
Bibliographical noteFunding Information:
Research reported in this publication was partially supported by the US Institute of Museum and Library Services (IMLS) National Leadership Grant #LG-81-16-0025, and the King Abdullah University of Science and Technology (KAUST).
Copyright © by SIAM.
- Bayesian personalized ranking
- Matrix factorization
- Network embedding
- Social recommender systems
- Top-k semantic friends
ASJC Scopus subject areas
- Computer Science Applications