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Using latent features for building an interpretable recommendation system

Ziming Zeng (School of Information Management, Wuhan University, Wuhan, China and Center for Studies of Information Resources, Wuhan University, Wuhan, China)
Yu Shi (School of Information Management, Wuhan University, Wuhan, China)
Lavinia Florentina Pieptea (Department of Information Science, University of North Texas, Denton, USA)
Junhua Ding (Department of Information Science, University of North Texas, Denton, USA)

The Electronic Library

ISSN: 0264-0473

Article publication date: 17 May 2021

Issue publication date: 17 July 2021

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Abstract

Purpose

Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation.

Design/methodology/approach

First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system

Findings

Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system.

Originality/value

This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.

Keywords

Acknowledgements

Fundings: This research was supported by National Natural Science Foundation of China (Grant # 71673203); Key Research Institutes of Philosophy and Social Science by Ministry of Education, PR China (Grant # 16JJD870003); World First Class Subject Foundation of Ministry of Education of China [Library, Information and Data Science]; China Scholarship Council (CSC)(Grant # 201906270043).

This paper were supported by all these four fundings, could we show all of them, thank you very much.

Citation

Zeng, Z., Shi, Y., Pieptea, L.F. and Ding, J. (2021), "Using latent features for building an interpretable recommendation system", The Electronic Library, Vol. 39 No. 2, pp. 281-295. https://doi.org/10.1108/EL-06-2020-0154

Publisher

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Emerald Publishing Limited

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