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Recommendation model based on multi-grained interaction that fuses users’ dynamic interests

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Abstract

Users leave many reviews while participating in network activities, and these have been proven to improve the performance of recommendation systems. However, most current works in the field of rating prediction only capture preference information from the single granularity of each review. This inevitably incurs the loss of fine-grained information in reviews. In addition, people do not realize that the usefulness of reviews is also affected by the dynamics of time. User interests continuously change over time, and reviews at different stages have different meanings for user interest modeling. Therefore, we propose a new dynamic multi-granular interaction recommendation (DMIR) model. First, the model does not aggregate reviews into a single vector but rather extracts the feature information from the multi-granular fragments of each review by dilated convolution. Second, the model uses multi-granular feature information to construct the personalized fine-grained interactions between users and items. At the same time, the model assigns each user’s dynamic interest representation according to the user’s interaction with the item at different stages and obtains the corresponding user-item interactions based on the dynamic interest. Finally, we design a gating mechanism to fuse the ratings obtained by two interactions for the final rating prediction. After extensive experiments conducted on five standard datasets based on Amazon, the results show that DMIR achieves a substantial improvement over the existing state-of-the-art models in terms of rating prediction. Additionally, fusing the user’s dynamic interest and multi-granular text interactions not only improves the performance of the model but also enhances the robustness of rating prediction.

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Data availability

The datasets generated during and analysed during the current study are available in the [Amazon Review Data] repository, [http://jmcauley.ucsd.edu/data/amazon/links.html].

Code availability

The relevant code for the paper is available.

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Funding

The authors are grateful to the Key Technology Research and Development Program of Shandong (2020CXGC010102) for funding.

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Authors and Affiliations

  1. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, Shandong, China

    Zhenyu Yang, Yu Wang & Xingang Wang

  2. College of Information Science and Engineering, Ocean University of China, Qingdao, 266100, Shandong, China

    Guojing Liu

  3. Department of Computer, Hong Kong Polytechnic University, Hongkong, China

    Zhe Li

Authors
  1. Zhenyu Yang
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  2. Yu Wang
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  3. Guojing Liu
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  4. Zhe Li
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  5. Xingang Wang
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Contributions

YZ: His contribution is providing the main innovative points of the model and the structure of the paper. WY: His contribution is to implement the proposed model and write the first draft of the paper. LG: His contribution is to analyze and process datasets. LZ: His contribution is to test the model performance and visualize the experimental results. WX: His contribution is to review and revise the first draft.

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Correspondence to Xingang Wang.

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Yang, Z., Wang, Y., Liu, G. et al. Recommendation model based on multi-grained interaction that fuses users’ dynamic interests. Int. J. Mach. Learn. & Cyber. 14, 3071–3085 (2023). https://doi.org/10.1007/s13042-023-01821-w

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