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Traditional recommendation systems mainly focus on modeling user interests.However, the dynamics of recommended items caused by attribute modifications(e.g. changes in prices) are also of great importance in real systems,especially in the fast-growing e-commerce environment, which may cause theusers' demands to emerge, shift and disappear. Recent studies that make effortson dynamic item representations treat the item attributes as side informationbut ignore its temporal dependency, or model the item evolution with a sequenceof related users but do not consider item attributes. In this paper, we proposeCore Attribute Evolution Network (CAEN), which partitions the user sequenceaccording to the attribute value and thus models the item evolution overattribute dynamics with these users. Under this framework, we further devise ahierarchical attention mechanism that applies attribute-aware attention foruser aggregation under each attribute, as well as personalized attention foractivating similar users in assessing the matching degree between target userand item. Results from the extensive experiments over actual e-commercedatasets show that our approach outperforms the state-of-art methods andachieves significant improvements on the items with rapid changes overattributes, therefore helping the item recommendation to adapt to the growth ofthe e-commerce platform.

CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment