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International Conference on Database Systems for Advanced Applications

DASFAA 2022: Database Systems for Advanced Applications pp 53–68Cite as

Inter- and Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13246))

Abstract

Cross-domain recommendation aims to model representations of users and items with the incorporation of additional knowledge from other domains, so as to alleviate the data sparsity issue. While recent studies demonstrate the effectiveness of cross-domain recommendation systems, there exist two unsolved challenges: (1) existing methods focus on transferring knowledge to generate shared factors implicitly, which fail to distill domain-shared features from explicit cross-domain correlations; (2) The majority of solutions are unable to effectively fuse domain-shared and domain-specific features. To this end, we propose Inter- and Intra-domain Relation-aware Cross-Domain Recommendation framework (\(I^2\)RCDR) to explicitly learn domain-shared representations by capturing high-order inter-domain relations. Specifically, we first construct a cross-domain heterogeneous graph and two single-domain heterogeneous graphs from ratings and reviews to preserve inter- and intra-domain relations. Then, a relation-aware graph convolutional network is designed to simultaneously distill domain-shared and domain-specific features, by exploring the multi-hop heterogeneous connections across different graphs. Moreover, we introduce a gating fusion mechanism to combine domain-shared and domain-specific features to achieve dual-target recommendation. Experimental results on public datasets show that the effectiveness of the proposed framework against many strong state-of-the-art methods.

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Notes

  1. 1.

    http://jmcauley.ucsd.edu/data/amazon/.

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Acknowledgments

This research is supported in part by the 2030 National Key AI Program of China 2018AAA0100503 (2018AAA0100500), National Science Foundation of China (No. 62072304, No. 61772341, No. 61832013), Shanghai Municipal Science and Technology Commission (No. 19510760500, No. 19511101500, No. 19511120300), the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (No. SL2020MS032), Scientific Research Fund of Second Institute of Oceanography, the open fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR, and Zhejiang Aoxin Co. Ltd.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Ke Wang, Yanmin Zhu, Haobing Liu, Tianzi Zang, Chunyang Wang & Kuan Liu

  2. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, MNR, Hangzhou, China

    Ke Wang, Yanmin Zhu, Tianzi Zang, Chunyang Wang & Kuan Liu

Authors
  1. Ke Wang
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  2. Yanmin Zhu
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  3. Haobing Liu
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  4. Tianzi Zang
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  5. Chunyang Wang
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  6. Kuan Liu
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Corresponding author

Correspondence to Yanmin Zhu .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science&Engr., Indian Institutes of Technology, Kanpur, Uttar Pradesh, India

    Arnab Bhattacharya

  2. National University of Singapore, Singapore, Singapore

    Janice Lee Mong Li

  3. University of California, Santa Barbara, Santa Barbara, CA, USA

    Divyakant Agrawal

  4. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  5. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Mukesh Mohania

  6. Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

  7. Indraprastha Institute of Information Te, New Delhi, India

    Vikram Goyal

  8. University of Aizu, Aizu, Japan

    Rage Uday Kiran

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Wang, K., Zhu, Y., Liu, H., Zang, T., Wang, C., Liu, K. (2022). Inter- and Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Recommendation. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-00126-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-00125-3

  • Online ISBN: 978-3-031-00126-0

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