Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Utilizing Popularity Characteristics for Product Recommendation
International Journal of Electronic Commerce
From hits to niches?: or how popular artists can bias music recommendation and discovery
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Extracting user profiles from large scale data
Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
On bootstrapping recommender systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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In this paper we propose a novel framework for modeling the uniqueness of the user preferences for recommendation systems. User uniqueness is determined by learning to what extent the user's item preferences deviate from those of an "average user" in the system. Based on this framework, we suggest three different recommendation strategies that trade between uniqueness and conformity. Using two real item datasets, we demonstrate the effectiveness of our uniqueness based recommendation framework.