Personalized recommendation on dynamic content using predictive bilinear models

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...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Wei Chu, Seung-Taek Park
• Keywords: Accurate Personalized Recommendations, Internet Technology, Predictive Bilinear Regression, Recommender Systems, WWW 2009