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Volume 35 Issue 6
November  2020
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Yu-Yao Liu, Bo Yang, Hong-Bin Pei, Jing Huang. Neural Explainable Recommender Model Based on Attributes and Reviews[J]. Journal of Computer Science and Technology, 2020, 35(6): 1446-1460. DOI: 10.1007/s11390-020-0152-8
Citation: Yu-Yao Liu, Bo Yang, Hong-Bin Pei, Jing Huang. Neural Explainable Recommender Model Based on Attributes and Reviews[J]. Journal of Computer Science and Technology, 2020, 35(6): 1446-1460. DOI: 10.1007/s11390-020-0152-8
shu

Neural Explainable Recommender Model Based on Attributes and Reviews

  • Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China

Funds: This work was supported by the University Science and Technology Research Plan Project of Jilin Province of China under Grant No. JJKH20190156KJ, the National Natural Science Foundation of China under Grant Nos. 61572226 and 61876069, Jilin Province Key Scientific and Technological Research and Development Project under Grant Nos. 20180201067GX and 20180201044GX, and Jilin Province Natural Science Foundation under Grant No. 20200201036JC.
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  • Author Bio:

    Yu-Yao Liu received his B.S. degree in computer science and technology from College of Computer Science and Technology at Jilin University, Changchun, in 2017. He is now a Master student at the same college, majoring in computer software and theory. His main research interests at the present stage include machine learning and recommender system.

  • Corresponding author:

    Jing Huang E-mail: huangjing@jlu.edu.cn

  • Received Date: October 31, 2019
  • Revised Date: May 06, 2020
  • Published Date: November 19, 2020
    Abstract
  • Explainable recommendation, which can provide reasonable explanations for recommendations, is increasingly important in many fields. Although traditional embedding-based models can learn many implicit features, resulting in good performance, they cannot provide the reason for their recommendations. Existing explainable recommender methods can be mainly divided into two types. The first type models highlight reviews written by users to provide an explanation. For the second type, attribute information is taken into consideration. These approaches only consider one aspect and do not make the best use of the existing information. In this paper, we propose a novel neural explainable recommender model based on attributes and reviews (NERAR) for recommendation that combines the processing of attribute features and review features. We employ a tree-based model to extract and learn attribute features from auxiliary information, and then we use a time-aware gated recurrent unit (T-GRU) to model user review features and process item review features based on a convolutional neural network (CNN). Extensive experiments on Amazon datasets demonstrate that our model outperforms the state-of-the-art recommendation models in accuracy of recommendations. The presented examples also show that our model can offer more reasonable explanations. Crowd-sourcing based evaluations are conducted to verify our model’s superiority in explainability.
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