Fast Online Learning Through Offline Initialization For Time- Sensitive Recommendation

Recommender problems with large and dynamic item pools are ubiquitous in web applications like news personalization, online advertising and web search. Despite the availability of rich item met-a-data, excess heterogeneity at the item level often requires inclusion of item-specific "factors" (or weights) in the model. However, since estimating item factors is computationally intensive, it poses a challenge for time-sensitive recommender problems where it is important to rapidly learn factors for new items (e. g. news articles, event updates, tweets) in an online fashion,In this paper, we propose a novel method called FOBFM (Fast Online Bilinear Factor Model) to learn item-specific factors quickly through online regression. The online regression for each item can be performed independently and hence the procedure is fast, scalable and easily parallelizable. However the convergence of these independent regressions can be slow due to high dimensionality. The central idea of our approach is to use a large amount of historical data to initialize the online models based on offline features and learn linear projections that can effectively reduce the dimensionality. We estimate the rank of our linear projections by taking recourse to online model selection based on optimizing predictive likelihood. Through extensive experiments, we show that our method significantly and uniformly outperforms other competitive methods and obtains relative lifts that are in the range of 10-15% in terms of predictive log-likelihood,200-300% for a rank correlation metric on a proprietary My Yahoo! dataset; it obtains 9% reduction in root mean squared error over the previously best method on a benchmark Movie Lens dataset using a time-based train/test data split.The work of my paper include as:1、Construct offline model:Extracting key features of news and users to achieve fast and accurate to meet the recommended requirements based on feature initialization and preparing for real-time recommendation.2、Construct online model:Based on the user’s immediate records, learning correction parameter model quickly in online fashion for fast convergence, build a complete personalized for each user profile.3、Design and implement the system and ensure performance meets the recommended requirements to achieve real-time for different users personalized news recommendation recommended solution, so that each user gets the news they are interested in a convenient, quick and easy way to discover new high-quality and interesting news.