Recommender systems with social regularization
Chinese University of Hong Kong · Microsoft (United States) · +1 more institution
Abstract
Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically…
Citation impact
- FWCI
- 197.11
- Percentile
- 100%
- References
- 45
Authors
5Topics & keywords
- Recommender system
- Computer science
- Matrix decomposition
- Regularization (linguistics)
- Artificial intelligence
- Information retrieval
- Machine learning