Long and short-term neural network news recommendation model based on self-attention mechanism

Information overload has become a huge barrier for people to effectively obtain information while enjoying the convenience of the big data era. Personalized news recommendation system can effectively help news platforms find articles that best suit user’s preferences from a large amount of news and enhance user experience. Existing news recommendation systems usually process user's preferences in a unified manner, ignoring the difference between long-term preferences and short-term preferences. In response to the problem above, this paper studies the long and short-term memory model based on GRU (Gated Recurrent Unit). This paper uses the LFM(Latent Factor Model) to extract user's long-term preferences and uses the GRU to obtain user’s short-term preferences from browsing history. Aiming at the problem of user interest transfer in the short term, this paper uses a self-attention mechanism based on time interval to characterize the degree of it. Experiments on two real-world datasets show our approach can effectively improve the performance of news recommendation.