PEN4Rec: Preference Evolution Networks for Session-based Recommendation
This work addresses the challenge of preference evolution for session-based recommendation systems, representing an incremental improvement over previous methods.
The paper tackles the problem of modeling evolving user preferences in session-based recommendation by proposing PEN4Rec, a two-stage retrieval network that captures preference dynamics and reduces disturbance from preference drifting, achieving superior performance on three public datasets.
Session-based recommendation aims to predict user the next action based on historical behaviors in an anonymous session. For better recommendations, it is vital to capture user preferences as well as their dynamics. Besides, user preferences evolve over time dynamically and each preference has its own evolving track. However, most previous works neglect the evolving trend of preferences and can be easily disturbed by the effect of preference drifting. In this paper, we propose a novel Preference Evolution Networks for session-based Recommendation (PEN4Rec) to model preference evolving process by a two-stage retrieval from historical contexts. Specifically, the first-stage process integrates relevant behaviors according to recent items. Then, the second-stage process models the preference evolving trajectory over time dynamically and infer rich preferences. The process can strengthen the effect of relevant sequential behaviors during the preference evolution and weaken the disturbance from preference drifting. Extensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed model.