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Knowledge-Rich Influence Propagation Recommendation Algorithm Based on Graph Attention Networks

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Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14176))

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Abstract

One of the biggest challenges in Recommendation Algorithms (RA) is how to obtain user and item embeddings from sparse interaction history. To take this challenge, most graph neural network based RAs explicitly incorporate high-order collaborative filtering signals on the user-item bipartite graph with either multi-layer semantics on the Knowledge Graph (KG) or multi-level neighbors on the social network. However, none of them fully integrate these three types of graph-structured data, which decreases embeddings’ precision. Based on this consideration, this paper integrates the three types of data by proposing a knowledge-rich influence propagation RA based on the graph attention mechanism. Specifically, in the semantic propagation, we categorize user preferences into deep interest obtained by multiple graph attention message propagations on related KG parts, and shallow interest generated from the interaction history. Moreover, the influence weight between items is determined by the number of co-interactions and the semantic similarity. These two factors as well as social relations together decide the influence weight between users. With these influence weights, final user and item embeddings are calculated through multi-layer message propagation. The experimental results show that the proposed recommendation algorithm outperforms several compelling baselines on six scaled-down real-world datasets. This work has confirmed the effectiveness of combining these three types of data to increase RAs’ coverage and accuracy.

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Acknowledgments

We are grateful to the reviewers of this paper for their constructive and insightful comments. The research reported in this paper was partially supported by the National Key R&D Program of China (NO. 2021YFB0300104), and the ANR project AGAPE ANR-18-CE23-0013.

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Authors and Affiliations

  1. Nankai University, Tianjin, China

    Yuping Yang, Guifei Jiang & Yuzhi Zhang

  2. Haihe Laboratory of Information Technology Application Innovation (HL-IT), Tianjin, China

    Guifei Jiang & Yuzhi Zhang

  3. Tianjin Key Laboratory of Operating System, Tianjin, China

    Guifei Jiang & Yuzhi Zhang

Authors
  1. Yuping Yang
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  2. Guifei Jiang
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  3. Yuzhi Zhang
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Corresponding author

Correspondence to Guifei Jiang .

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

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Yang, Y., Jiang, G., Zhang, Y. (2023). Knowledge-Rich Influence Propagation Recommendation Algorithm Based on Graph Attention Networks. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14176. Springer, Cham. https://doi.org/10.1007/978-3-031-46661-8_10

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  • DOI: https://doi.org/10.1007/978-3-031-46661-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46660-1

  • Online ISBN: 978-3-031-46661-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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