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Explainable recommendation with fusion of aspect information

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Abstract

Explainable recommendation has attracted increasing attention from researchers. The existing methods, however, often suffer from two defects. One is the lack of quantitative fine-grained explanations why a user chooses an item, which likely makes recommendations unconvincing. The other one is that the fine-grained information such as aspects of item is not effectively utilized for making recommendations. In this paper, we investigate the problem of making quantitatively explainable recommendation at aspect level. It is a nontrivial task due to the challenges on quantitative evaluation of aspect and fusing aspect information into recommendation. To address these challenges, we propose an Aspect-based Matrix Factorization model (AMF), which is able to improve the accuracy of rating prediction by collaboratively decomposing the rating matrix with the auxiliary information extracted from aspects. To quantitatively evaluate aspects, we propose two metrics: User Aspect Preference (UAP) and Item Aspect Quality (IAQ), which quantify user preference to a specific aspect and the review sentiment of item on an aspect, respectively. By UAP and IAQ, we can quantitatively explain why a user chooses an item. To achieve information incorporation, we assemble UAPs and IAQs into two matrices UAP Matrix (UAPM) and IAQ Matrix (IAQM), respectively, and fuse UAPM and IAQM as constraints into the collaborative decomposition of item rating matrix. The extensive experiments conducted on real datasets verify the recommendation performance and explanatory ability of our approach.

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Acknowledgments

This work is supported by NSFC through grant 61173099, Ministry of Education of China through 6141A02033304, and in part by NSF through grants IIS-1526499 and CNS-1626432.

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, China

    Yunfeng Hou, Ning Yang & Yi Wu

  2. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, 60607, USA

    Philip S. Yu

  3. Institute for Data Science, Tsinghua University, Beijing, China

    Philip S. Yu

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  1. Yunfeng Hou
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  2. Ning Yang
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  4. Philip S. Yu
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Correspondence to Ning Yang.

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Hou, Y., Yang, N., Wu, Y. et al. Explainable recommendation with fusion of aspect information. World Wide Web 22, 221–240 (2019). https://doi.org/10.1007/s11280-018-0558-1

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