GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Estimating sharer reputation via social data calibration
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting trustors as well as trustees in trust-based recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Recommendation in online shopping malls: results and experiences
Proceedings of the 2013 Research in Adaptive and Convergent Systems
| Hi-index | 0.00 |
A variety of recommendation methods have been proposed to satisfy the performance and accuracy; however, it is fairly difficult to satisfy both of them because there is a trade-off between them. In this paper, we introduce the notion of category experts and propose the recommendation method by exploiting the ratings of category experts instead of those of the users similar to a target user. We also extend the method that uses both the category preference of a target user and his/her similarity to category experts. We show that our method significantly outperforms the existing methods in terms of performance and accuracy through extensive experiments with real-world data.