Personalized active learning for collaborative filtering Export

@inproceedings{citeulike:3112644, abstract = {Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most informative set of training examples through minimum interactions with the users. Current active learning approaches in CF make an implicit and unrealistic assumption that a user can provide rating for any queried item. This paper introduces a new approach to the problem which does not make such an assumption. We personalize active learning for the user, and query for only those items which the user can provide rating for. We propose an extended form of Bayesian active learning and use the Aspect Model for CF to illustrate and examine the idea. A comparative evaluation of the new method and a well-established baseline method on benchmark datasets shows statistically significant improvements with our method over the performance of the baseline method that is representative for existing approaches which do not take personalization into account.}, address = {New York, NY, USA}, author = {Harpale, Abhay S. and Yang, Yiming}, booktitle = {SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval}, citeulike-article-id = {3112644}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1390352}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1390334.1390352}, doi = {10.1145/1390334.1390352}, isbn = {9781605581644}, keywords = {active-annotation, collaborative\_filtering, personalization}, location = {Singapore, Singapore}, pages = {91--98}, posted-at = {2008-08-12 20:15:08}, priority = {0}, publisher = {ACM}, title = {Personalized active learning for collaborative filtering}, url = {http://dx.doi.org/10.1145/1390334.1390352}, year = {2008} }

TY - CONF ID - citeulike:3112644 L3 - citeulike-article-id:3112644 N2 - Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most informative set of training examples through minimum interactions with the users. Current active learning approaches in CF make an implicit and unrealistic assumption that a user can provide rating for any queried item. This paper introduces a new approach to the problem which does not make such an assumption. We personalize active learning for the user, and query for only those items which the user can provide rating for. We propose an extended form of Bayesian active learning and use the Aspect Model for CF to illustrate and examine the idea. A comparative evaluation of the new method and a well-established baseline method on benchmark datasets shows statistically significant improvements with our method over the performance of the baseline method that is representative for existing approaches which do not take personalization into account. CY - Singapore, Singapore SN - 9781605581644 AD - New York, NY, USA EP - 98 TI - Personalized active learning for collaborative filtering PB - ACM T2 - SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval SP - 91 KW - active-annotation KW - collaborative_filtering KW - personalization AU - Harpale, Abhay AU - Yang, Yiming PY - 2008/// UR - http://dx.doi.org/10.1145/1390334.1390352 ER -