Abstract
Personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information around the world. A variety of news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing the implicit “social” factors (i.e., the potential influential experts in news reading community) among news readers to facilitate news personalization. In this paper, we investigate the feasibility of integrating content-based methods, collaborative filtering and information diffusion models by employing probabilistic matrix factorization techniques. We propose PRemiSE, a novel Personalized news Recommendation framework via implicit Social Experts, in which the opinions of potential influencers on virtual social networks extracted from implicit feedbacks are treated as auxiliary resources for recommendation. We evaluate and compare our proposed recommendation method with various baselines on a collection of news articles obtained from multiple popular news websites. Experimental results demonstrate the efficacy and effectiveness of our method, particularly, on handling the so-called cold-start problem.
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