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DLSA: dual-learning based on self-attention for rating prediction

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Abstract

Latent factor models (LFMs) have been widely applied in many rating recommendation systems because of their prediction rating capability. Nevertheless, LFMs may not fully leverage rating information and lack good recommendation performance. Furthermore, many subsequent works have often used auxiliary text information, such as user attributes, to improve the prediction effect. However, they did not fully utilize implicit information (i.e., users’ preferences, items’ common features), and additional information is sometimes difficult to acquire. In this paper, we propose a new framework, named dual-learning based on self-attention for rating prediction (DLSA), to solve these problems. Self-attention has a proven ability to learn implicit information about sentences in machine translation, which can be used to mine implicit information in recommendation systems. Additionally, dual learning has shown that the model can generate feedback information when it learns from unlabeled data; therefore, we were inspired to use it in recommendation and obtain implicit information feedback. From the user’s perspective, we design a user self-attention model to learn user-user implicit information and create an interactive user-item self-attention mechanism to learn user-item information. We can also obtain item self-attention to utilize item-item information and an item-user self-attention model to acquire item-user information from an item’s perspective. The interactive structure of the user-item and item-user can adopt the dual learning mechanism to learn implicit information feedback. Moreover, no auxiliary text information was used in the process. The proposed model combines the power of self-attention for implicit information and dual learning for information feedback in a new neural network architecture. Experiments on several real-world datasets demonstrate the effectiveness of DLSA over competitive algorithms on rating recommendation.

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Notes

  1. https://www.tensorflow.org.

  2. https://grouplens.org/datasets/movielens/.

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Acknowledgements

This work was jointly supported by the National Key Research and Development Program of China (2017YFB1401903), the Natural Science Foundation of China (Nos. 61673020, 61702003, 61876001), and the Natural Science Foundation of Anhui Province (1808085M-F175). The authors would also like to thank the anonymous reviewers for their valuable comments.

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Author notes

  1. Fulan Qian and Jianhong Li have contributed equally.

Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, China

    Fulan Qian, Yafan Huang, Jianhong Li, Shu Zhao & Yanping Zhang

  2. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Hefei, China

    Fulan Qian, Yafan Huang, Jianhong Li, Shu Zhao & Yanping Zhang

  3. School of Artificial Intelligence, Anhui University of Science and Technology, Huainan, China

    Jianhong Li & Chengjun Wang

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  1. Fulan Qian
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Correspondence to Fulan Qian.

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Qian, F., Huang, Y., Li, J. et al. DLSA: dual-learning based on self-attention for rating prediction. Int. J. Mach. Learn. & Cyber. 12, 1993–2005 (2021). https://doi.org/10.1007/s13042-021-01288-7

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  • DOI: https://doi.org/10.1007/s13042-021-01288-7

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