Personalized recommendation on dynamic content using predictive bilinear models Export

@inproceedings{Chu2009recom, abstract = {In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches.}, address = {New York, NY, USA}, author = {Chu, Wei and Park, Seung T.}, booktitle = {WWW '09: Proceedings of the 18th international conference on World wide web}, citeulike-article-id = {5670637}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1526709.1526802}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1526709.1526802}, doi = {10.1145/1526709.1526802}, isbn = {978-1-60558-487-4}, keywords = {content, dynamic, personalized, recommendation}, location = {Madrid, Spain}, pages = {691--700}, posted-at = {2009-11-09 14:51:10}, priority = {2}, publisher = {ACM}, title = {Personalized recommendation on dynamic content using predictive bilinear models}, url = {http://dx.doi.org/10.1145/1526709.1526802}, year = {2009} }

TY - CONF ID - Chu2009recom L3 - citeulike-article-id:5670637 N2 - In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches. CY - Madrid, Spain SN - 978-1-60558-487-4 AD - New York, NY, USA EP - 700 TI - Personalized recommendation on dynamic content using predictive bilinear models PB - ACM T2 - WWW '09: Proceedings of the 18th international conference on World wide web SP - 691 KW - content KW - dynamic KW - personalized KW - recommendation AU - Chu, Wei AU - Park, Seung PY - 2009/// UR - http://dx.doi.org/10.1145/1526709.1526802 ER -