Discovery-oriented collaborative filtering for improving user satisfaction Export

@inproceedings{citeulike:5206812, 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.}, address = {New York, NY, USA}, author = {Hijikata, Yoshinori and Shimizu, Takuya and Nishida, Shogo}, booktitle = {IUI '09: Proceedings of the 13th international conference on Intelligent user interfaces}, citeulike-article-id = {5206812}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1502650.1502663}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1502650.1502663}, doi = {10.1145/1502650.1502663}, isbn = {978-1-60558-168-2}, keywords = {collaborative\_filtering}, location = {Sanibel Island, Florida, USA}, pages = {67--76}, posted-at = {2009-07-22 08:41:00}, priority = {2}, publisher = {ACM}, title = {Discovery-oriented collaborative filtering for improving user satisfaction}, url = {http://dx.doi.org/10.1145/1502650.1502663}, year = {2009} }

TY - CONF ID - citeulike:5206812 L3 - citeulike-article-id:5206812 N2 - 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. CY - Sanibel Island, Florida, USA SN - 978-1-60558-168-2 AD - New York, NY, USA EP - 76 TI - Discovery-oriented collaborative filtering for improving user satisfaction PB - ACM T2 - IUI '09: Proceedings of the 13th international conference on Intelligent user interfaces SP - 67 KW - collaborative_filtering AU - Hijikata, Yoshinori AU - Shimizu, Takuya AU - Nishida, Shogo PY - 2009/// UR - http://dx.doi.org/10.1145/1502650.1502663 ER -