We propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse personal digital traces into recommendations. Our contextaware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. Specifically, in this work we target personalized news and local event recommendations for their utility and societal importance. We evaluated the model with a large-scale offline evaluation leveraging users' public Twitter traces. In addition, we conducted a direct evaluation of the model's recommendations in a 33-participant study using Twitter, Facebook and email traces. In the both cases, the proposed model showed significant improvement over the stateof-the-art algorithms, suggesting the value of using this new usercentric recommendation model to improve recommendation quality, including in cold-start situations.
Hsieh et al. REF propose a user-centric recommendation model that considers both content information and user social relations.
Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces
WWW
Computer Science