Dig users’ intentions via attention flow network for personalized recommendation

Accurately forecasting user’s purchase intention over time is a huge challenge for personalized recommend systems, of which a critical problem is how to model the changes of user preference and temporal correlation of items. In this paper, aiming at addressing this question, we first introduce attention flow network to model users’ purchase records by leveraging attention flow that describes the changing process of purchase intention. Then based on the attention flow network and individuals’ attention flows, we propose a novel personalized recommendation algorithm named Attention Flow Network based Personalized Recommendation (AFNPR). Our method integrates all the purchase sequences of users into a weighted attention flow network, and recommends items based on transition probabilities related to attention flow network with user’s attention decay, which is efficient in linear time. The experiments demonstrate its superior performance on several real datasets.