Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
A Hybrid Collaborative Filtering System for Contextual Recommendations in Social Networks
DS '09 Proceedings of the 12th International Conference on Discovery Science
Design and evaluation of a command recommendation system for software applications
ACM Transactions on Computer-Human Interaction (TOCHI)
Looking for "good" recommendations: a comparative evaluation of recommender systems
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
ACM Transactions on Interactive Intelligent Systems (TiiS)
From popularity to personality: a heuristic music recommendation method for niche market
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
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Clustering has been a widely applied approach to improve the computation efficiency of collaborative filtering based recommendation systems. Many techniques have been suggested to discover the item-to-item, user-to-user, and item-to-user associations within user clusters. However, there are few systems utilize the cluster based topic-to-topic associations to make recommendations. This paper suggests a taxonomy-based recommender system that utilizes cluster based topic-to-topic associations to improve its recommendation quality and novelty.