Communications of the ACM
Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Improving the Recommendation of Collaborative Filtering by Fusing Trust Network
CIS '12 Proceedings of the 2012 Eighth International Conference on Computational Intelligence and Security
Hi-index | 0.00 |
To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely adopted recommender algorithms, whereas it is suffering the issues of data sparsity and cold start that will severely degrade quality of recommendations. To address such issues, this article proposes a novel method, trying to improve the performance of collaborative filtering recommendation by means of elaborately integrating twofold sparse information, the conventional rating data given by users and the social trust network among the same users. It is a model-based method adopting matrix factorization technique to map users into low-dimensional latent feature spaces in terms of their trust relationship, aiming to reflect users' reciprocal influence on their own opinions more reasonably. The validations against a real-world dataset show that the proposed method performs much better than state-of-the-art recommendation algorithms for social collaborative filtering by trust.