GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
IEEE Transactions on Knowledge and Data Engineering
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
A hybrid approach to item recommendation in folksonomies
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
The effect of sparsity on collaborative filtering metrics
ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
Proceedings of the fourth ACM conference on Recommender systems
Item Recommendation in Collaborative Tagging Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
KMulE: a framework for user-based comparison of recommender algorithms
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
| Hi-index | 0.00 |
Movie recommender systems attempt to find movies which are of interest for their users. However, as new movies are added, and new users join movie recommendation services, the problem of recommending suitable items becomes increasingly harder. In this paper, we present a simple way of using a priori movie data in order to improve the accuracy of collaborative filtering recommender systems. The approach decreases the sparsity of the rating matrix by inferring personal ratings on tags assigned to movies. The new tag ratings are used to find which movies to recommend. Experiments performed on data from the movie recommendation community Moviepilot show a positive effect on the quality of recommended items.