  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.
