creators_name: Chu, Wei creators_name: Park, Seung-Taek type: conference_item datestamp: 2009-04-06 19:10:33 lastmod: 2009-04-22 10:15:47 metadata_visibility: show title: Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models ispublished: pub full_text_status: public pres_type: paper 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. date: 2009-04 pagerange: 691-691 event_title: 18th International World Wide Web Conference event_location: Madrid, Spain event_dates: April 20th-24th, 2009 event_type: conference refereed: TRUE citation: Chu, Wei and Park, Seung-Taek (2009) Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain. document_url: http://www2009.eprints.org/70/1/p691.pdf document_url: http://www2009.eprints.org/70/2/personalization.ppt