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Combining Meta-Graph and Attention for Recommendation over Heterogenous Information Network

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Book cover Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11446))

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Abstract

Recently heterogeneous information network (HIN) has gained wide attention in recommender systems due to its flexibility in modeling rich objects and complex relationships. It’s still challenging for HIN based recommenders to capture high-level structure and fuse the mined features of users and items effectively. In this paper, we propose an approach for the recommendation over HIN, called MGAR, which combines Meta-Graph and Attention to address the challenge. Informally speaking, meta-graph is applied to feature extraction, so as to capture more semantic information, while the attention mechanism is used to fuse the features arising from different meta-graphs. MGAR can be divided into two stages. In the first stage, we apply the matrix factorization technique to generate latent factors based on predefined meta-graphs. In the second stage, the embeddings of users and items are fused with the neural attention mechanism. And then the deep neural network is employed to make recommendations by modeling complicated interactions. Experiments over two real datasets indicate MGAR achieves state-of-the-art performance.

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Notes

  1. 1.

    http://www.yelp.com.

  2. 2.

    http://www.yelp.com/dataset/.

  3. 3.

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

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China under Grant No. 61572002, No. 61170300, No. 61690201, and No. 61732001.

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Authors and Affiliations

  1. School of Mathematical Sciences, Peking University, Beijing, 100871, China

    Chenfei Zhao, Hengliang Wang, Yuan Li & Kedian Mu

Authors
  1. Chenfei Zhao
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  2. Hengliang Wang
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  3. Yuan Li
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  4. Kedian Mu
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Corresponding author

Correspondence to Chenfei Zhao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Zhao, C., Wang, H., Li, Y., Mu, K. (2019). Combining Meta-Graph and Attention for Recommendation over Heterogenous Information Network. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_23

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  • DOI: https://doi.org/10.1007/978-3-030-18576-3_23

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  • Online ISBN: 978-3-030-18576-3

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