MBPI: Mixed behaviors and preference interaction for session-based recommendation

Abstract

Session-based recommendation is a task to recommend the next clicked item when the user’s current interaction sequence is given. Accurately modeling the session representation is critical for session-based recommendation. However, we find that most current methods for session-based recommendation just use conscious behavior and information in the current session, ignoring the information of unconscious behavior in the current session and preference interaction with neighborhood sessions. In this paper, we propose a Mixed Behaviors and Preference Interaction model (MBPI), which utilizes conscious and unconscious behaviors and parallel co-attention mechanism, for session-based recommendation. In MBPI, we apply a Gated Recurrent Unit (GRU) to generate the session global preference, and employ another GRU with an item-level attention mechanism to explore the session local preference, with the multi-feature behaviors. Then, we introduce a parallel co-attention mechanism to capture the preference interaction with the help of the current session and neighborhood sessions and to update the two preferences of the current session. Finally, we combine the session global preference and session local preference as session representation and make recommendation. Experimental results on three real-world datasets show our method outperforms the state-of-the-art methods and validate the effectiveness of our approach.