GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Top-N recommendation through belief propagation
Proceedings of the 21st ACM international conference on Information and knowledge management
On using category experts for improving the performance and accuracy in recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
Recommendation in online shopping malls: results and experiences
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Recommendation in online shopping malls: results and experiences
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Hi-index | 0.00 |
In a trust network, two users who are connected by a trust relationship tend to have similar interests. Based on this observation, existing trust-aware recommendation methods predict ratings for a target user on unseen items by referencing to ratings of those users who are reachable from the target user in the forward direction of trustor-trustee relationship through the trust network. However, these methods have overlooked the possibility of utilizing the ratings of those users reachable in the backward direction, which may also have similar interests. In this paper, we investigate this possibility by identifying and adding these users to the existing methods when predicting ratings for the target user. We perform a series of experiments and observe that our approach improves the coverage while preserving the accuracy.