Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Reducing buyer search costs: implications for electronic marketplaces
Management Science - Special issue: Frontier research on information systems and economics
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
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
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
Getting recommender systems to think outside the box
Proceedings of the third ACM conference on Recommender systems
The million dollar programming prize
IEEE Spectrum
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
The effect of context-aware recommendations on customer purchasing behavior and trust
Proceedings of the fifth ACM conference on Recommender systems
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Persuasive Recommender Systems: Conceptual Background and Implications
Persuasive Recommender Systems: Conceptual Background and Implications
Proceedings of the 7th ACM conference on Recommender systems
Beyond rating prediction accuracy: on new perspectives in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
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This paper proposes a number of studies in order to move the field of recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy. We contribute to existing helpful but less explored recommendation strategies and propose new approaches targeting to 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. The overall goal of this research program is to expand our focus from even more accurate rating predictions toward a more holistic experience for the users, by providing them with non-obvious but high quality recommendations and avoiding the over-specialization and concentration bias problems. In particular, we propose a new probabilistic neighborhood-based method as an improvement of the standard $k$-nearest neighbors approach, alleviating some of the most common problems of collaborative filtering recommender systems, based on classical metrics of dispersion and diversity as well as some newly proposed metrics. Furthermore, we propose a concept of unexpectedness in recommender systems and operationalize it by suggesting various mechanisms for specifying the expectations of the users and proposing a recommendation method for providing the users with unexpected but high quality personalized recommendations that fairly match their interests. Besides, in order to generate utility-based recommendations for Massive Open Online Courses (MOOCs) that better serve the educational needs of students, we study the satisfaction of users with online courses vis-a-vis student retention. Finally, we summarize the conclusions of the conducted studies, discuss the limitations of our work and also outline the managerial implications of the proposed stream of research.