TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Instanced-Based Mapping between Thesauri and Folksonomies
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Tag Based Collaborative Filtering for Recommender Systems
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Novel Item Recommendation by User Profile Partitioning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Personalized Recommender Systems Integrating Social Tags and Item Taxonomy
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Using Tag Co-occurrence for Recommendation
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Content-based recommendation systems
The adaptive web
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
Topic diversity in tag recommendation
Proceedings of the 7th ACM conference on Recommender systems
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Collaborative tagging has emerged as a mechanism to describe items in large on-line collections. Tags are assigned by users to describe and find back items, but it is also tempting to describe the users in terms of the tags they assign or in terms of the tags of the items they are interested in. The tag-based profile thus obtained can be used to recommend new items. If we recommend new items by computing their similarity to the user profile or to all items seen by the user, we run into the risk of recommending only neutral items that are a bit relevant for each topic a user is interested in. In order to increase user satisfaction many recommender systems not only optimize for accuracy but also for diversity. Often it is assumed that there exists a trade-off between accuracy and diversity. In this paper we introduce topic aware recommendation algorithms. Topic aware algorithms first detect different interests in the user profile and then generate recommendations for each of these interests. We study topic aware variants of three tag based recommendation algorithms and show that each of them gives better recommendations than their base variants, both in terms of precision and recall and in terms of diversity.