Personalized multi-head self-attention network for news recommendation.

With the rapid explosion of online news and user population, personalized news recommender systems have proved to be efficient ways of alleviating information overload problems by suggesting information which attracts users in line with their tastes. Exploring relationships among words and news is critical to structurally model users' latent tastes including interested domains, while selecting informative words and news can directly reflect users' interests. Most of the current studies do not provide an effective framework that combines distilling users' interested latent spaces and explicit points systematically. Moreover, introducing more advanced techniques to merely chase accuracy has become a universal phenomenon. In this study, we design a Personalized Multi-Head Self-Attention Network (PMSN) for news recommendation, which combines multi-head self-attention network with personalized attention mechanism from both word and news levels. Multi-head self-attention mechanism is used to model interactions among words and news, exploring latent interests. Personalized attention mechanism is applied by embedding users' IDs to highlight informative words and news, which can enhance the interpretability of personalization. Comprehensive experiments conducted using two real-world datasets demonstrate that PMSN efficiently outperforms state-of-the-art methods in terms of recommendation accuracy, without complicated structure design and exhausted even external resources consumption. Furthermore, visualized case study validates that attention mechanism indeed increases the interpretability.