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Contrastive Hierarchical Gating Networks for Rating Prediction

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Neural Information Processing (ICONIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14449))

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Abstract

Review-based recommendations suffer from text noises and the absence of supervised signals. To address those challenges, we propose a novel hierarchical gated sentiment-aware model for rating prediction in this paper. Specifically, to automatically suppress the influence of noisy reviews, we propose a hierarchical gating network to select informative textual signals at different levels of granularity. Specifically, a local gating module is proposed to select reviews with personalized end-to-end differential thresholds. The aim is to gate reviews in a relatively “hard” way to minimize the information flow from noisy reviews while facilitating the model training. A global gating module is employed to evaluate the overall usefulness of the review signals by estimating the uncertainties encoded in the historical reviews. In addition, a discriminative learning module is proposed to supervise the learning of the hierarchical gating network. The essential intuition is to exploit the sentiment consistencies between the target reviews and the target ratings for developing self-supervision signals so that the hierarchical gating network can select relevant reviews related to the target ratings for better prediction. Finally, extensive experiments on public datasets and comparison studies with state-of-the-art baselines have demonstrated the effectiveness of the proposed model, additional investigations also provide a deep insight into the rationale underlying the superiority of the proposed model.

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Notes

  1. 1.

    http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/.

  2. 2.

    https://tensorflow.google.cn/api_docs.

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Acknowledgements

Funding: This work was partially supported by Shandong Provincial Natural Science Foundation (ZR2021QF014), the National Natural Science Foundation of China (62102437), Fundamental Research Funds for the Central Universities (SWU021001), Beijing Nova Program (Z211100002121116, 2021108), Oversea Study and Innovation Foundation of Chongqing (CX2021105). Conflict of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Authors and Affiliations

  1. Shandong Normal University, Shandong, China

    Jingwei Ma

  2. Defense Innovation Institute, Beijing, China

    Jiahui Wen, Chenglong Huang, Lu Wang & Guangda Zhang

  3. Southwest University, Chongqing, China

    Mingyang Zhong

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  1. Jingwei Ma
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  2. Jiahui Wen
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  3. Chenglong Huang
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  4. Mingyang Zhong
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  5. Lu Wang
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  6. Guangda Zhang
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Correspondence to Jiahui Wen .

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Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Ma, J., Wen, J., Huang, C., Zhong, M., Wang, L., Zhang, G. (2024). Contrastive Hierarchical Gating Networks for Rating Prediction. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_32

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