An algorithm for suffix stripping
Readings in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Robustness of collaborative recommendation based on association rule mining
Proceedings of the 2007 ACM conference on Recommender systems
Effective explanations of recommendations: user-centered design
Proceedings of the 2007 ACM conference on Recommender systems
Proceedings of the 2007 international ACM conference on Supporting group work
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Integrating tags in a semantic content-based recommender
Proceedings of the 2008 ACM conference on Recommender systems
Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
Learning to recognize valuable tags
Proceedings of the 14th international conference on Intelligent user interfaces
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
TagRec: Leveraging Tagging Wisdom for Recommendation
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
The impact of ambiguity and redundancy on tag recommendation in folksonomies
Proceedings of the third ACM conference on Recommender systems
TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
A tag recommendation system for folksonomy
Proceedings of the 2nd ACM workshop on Social web search and mining
Collaborative tagging in recommender systems
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Revyu.com: a reviewing and rating site for the web of data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Tag expression: tagging with feeling
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Recommender Systems: An Introduction
Recommender Systems: An Introduction
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Accuracy improvements for multi-criteria recommender systems
Proceedings of the 13th ACM Conference on Electronic Commerce
How should I explain? A comparison of different explanation types for recommender systems
International Journal of Human-Computer Studies
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In recent years, different proposals have been made to exploit Social Web tagging information to build more effective recommender systems. The tagging data, for example, were used to identify similar users or were viewed as additional information about the recommendable items. Recent research has indicated that “attaching feelings to tags” is experienced by users as a valuable means to express which features of an item they particularly like or dislike. When following such an approach, users would therefore not only add tags to an item as in usual Web 2.0 applications, but also attach a preference (affect) to the tag itself, expressing, for example, whether or not they liked a certain actor in a given movie. In this work, we show how this additional preference data can be exploited by a recommender system to make more accurate predictions. In contrast to previous work, which also relied on so-called tag preferences to enhance the predictive accuracy of recommender systems, we argue that tag preferences should be considered in the context of an item. We therefore propose new schemes to infer and exploit context-specific tag preferences in the recommendation process. An evaluation on two different datasets reveals that our approach is capable of providing more accurate recommendations than previous tag-based recommender algorithms and recent tag-agnostic matrix factorization techniques.