LAUSR

Abstract Personalized recommendation is one of the most effective ways to alleviate the problem of information explosion. Recurrent Neural Network (RNN) sequential recommendation based on user preference modeling has been widely investigated recently. In this paper, a Hierarchical Time-based Directional Attention (HTDA) network is proposed to enhance sequential recommendation by applying fine-grained user intention representation and dynamic user preference representation with rich global sequential interaction features. Our analyses and results significantly differ from earlier related works in two aspects: 1) We put forward a session-level representation method, based on multi-dimensional attention mechanism, to enhance the degree of matching between the user interaction sequence and the user intention and reduce the impact of noise interaction; 2) We propose to integrate a time-based directional attention mechanism into RNN, to capture the sequential patterns of interaction sessions more effectively and improve the understanding of user dynamic preferences. The main contribution of our work is to effectively combine the accurate description of user intentions and the powerful expression of user dynamic preferences through a hierarchical attention architecture. In turn, our architecture can provide a more favorable framewrok for further enhancing the recommendation results. Extensive experiments are conducted on two public benchmark datasets, and very promising performance can be achieved with our proposed HTDA-based framework.