A Recommendations Model with Multiaspect Awareness and Hierarchical User-Product Attention Mechanisms

Zhongqin Bi ; Shuming Dou ; Zhe liu ; Yongbin Li

Computer Science and Information Systems, Tome 17 (2020) no. 3.

Résumé

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.

Détail

Keywords: recommendation, long short-term memory, attention mechanism, deep learning, rating prediction