Abstract:
Many reviews-rating integration studies have improved recommendation performance. These works still have flaws: (1) Dynamically integrating review and interaction data features is often overlooked, but treating them equally may lead to a faulty understanding of user preferences. (2) Soft attention methods model word local semantic information. The attention map may contain irrelevant features. We propose AHAG, an adaptive hierarchical attention-enhanced gated network that integrates reviews for item recommendation. Adaptively incorporating reviews into AHAG captures users’ hidden intentions. We design a gated network to dynamically fuse extracted features and select user-preference-relevant features. A hierarchical attention mechanism learns important semantic information features and their dynamic interaction to distinguish fine-grained features. To predict ratings, neural factorization machines use high-order non-linear interaction. The proposed AHAG significantly outperforms state-of-the-art methods on seven real-world datasets. To improve recommendation task interpretability, the attention mechanism can highlight relevant review information.
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