document: 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 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 .
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