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

TL;DR

This paper introduces CAEN, a hierarchical attentive network that models item attribute evolution over time to improve recommendation accuracy in fast-changing e-commerce environments.

Contribution

The paper proposes a novel hierarchical attention-based model that captures temporal attribute dynamics and user-item interactions for more adaptive recommendations.

Findings

Outperforms state-of-the-art methods on real e-commerce datasets.

Significantly improves recommendations for items with rapid attribute changes.

Effectively models item evolution considering attribute dependencies.

Abstract

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 the users' demands to emerge, shift and disappear. Recent studies that make efforts on dynamic item representations treat the item attributes as side information but ignore its temporal dependency, or model the item evolution with a sequence of related users but do not consider item attributes. In this paper, we propose Core Attribute Evolution Network (CAEN), which partitions the user sequence according to the attribute value and thus models the item evolution over attribute dynamics with these users. Under this framework, we further devise a hierarchical attention mechanism that applies attribute-aware attention for user aggregation under each attribute, as well as personalized attention for activating similar users in assessing the matching degree between target user and item. Results from the extensive experiments over actual e-commerce datasets show that our approach outperforms the state-of-art methods and achieves significant improvements on the items with rapid changes over attributes, therefore helping the item recommendation to adapt to the growth of the e-commerce platform.