Inproceedings,
Discovery-oriented collaborative filtering for improving user satisfaction
IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces, page 67--76. New York, NY, USA, ACM, (2008)
DOI: http://doi.acm.org/10.1145/1502650.1502663
Abstract
Many recommender systems employed in commercial web sites use collaborative filtering. The main goal of traditional collaborative filtering techniques is improvement of the accuracy of recommendation. Nevertheless, such techniques present the problem that they include many items that the user already knows. These recommendations appear to be good when we consider accuracy alone. On the other hand, when we consider users' satisfaction, they are not necessarily good because of the lack of discovery. In our work, we infer items that a user does not know by calculating the similarity of users or items based on information about what items users already know. We seek to recommend items that the user would probably like and does not know by combining the above method and the most popular method of collaborative filtering.
