GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
IEEE Transactions on Knowledge and Data Engineering
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Mixing it up: recommending collections of items
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommendations with prerequisites
Proceedings of the third ACM conference on Recommender systems
The million dollar programming prize
IEEE Spectrum
Breaking out of the box of recommendations: from items to packages
Proceedings of the fourth ACM conference on Recommender systems
Recommender Systems: An Introduction
Recommender Systems: An Introduction
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
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM international conference on Web search and data mining
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This paper proposes a number of studies in order to move recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy. We contribute to existing helpful but less explored paradigms and also propose new approaches aiming at more useful recommendations for both users and businesses. Working toward this direction, we discuss the studies we have conducted so far and present our future research plans. In particular, we move our focus from even more accurate rating predictions and aim at offering a holistic experience to the users by avoiding the over-specialization of generated recommendations and providing the users with sets of non-obvious but high quality recommendations that fairly match their interests and they will remarkably like.