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Heterogeneous Graph Representation for Recommendation

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Heterogeneous Graph Representation Learning and Applications

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Abstract

With the rapid development of web services, various kinds of useful auxiliary data (a.k.a., side information) become available in recommender systems. To characterize these complex and heterogeneous auxiliary data, heterogeneous graph (HG) representation methods have been widely adopted due to the flexibility in modeling data heterogeneity. In this chapter, we introduce three HG representation based recommendation systems solving the unique challenges existing in diverse real-world scenarios, including Top-N recommendation (MCRec), cold-start recommendation (MetaHIN), and bibliographic recommendation (ASI). In the field of HG representation for recommendation, methods mainly contain three key components: HG constructions, HG representation learning and recommendation based on the HG representation.

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Notes

  1. 1.

    https://grouplens.org/datasets/movielens/.

  2. 2.

    https://www.last.fm.

  3. 3.

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

  4. 4.

    We hold out 10% training data as the validation set for parameter tuning.

  5. 5.

    https://book.douban.com.

  6. 6.

    https://grouplens.org/datasets/movielens/.

  7. 7.

    https://www.yelp.com/dataset/challenge.

  8. 8.

    AI: ICML, AAAI, IJCAI, NIPS. DM: KDD, WSDM, ICDM, PKDD. DB: SIGMOD, VLDB, ICDE. IS: SIGIR, CIKM.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, School of Computer Science, Beijing, China

    Chuan Shi

  2. Beijing University of Posts and Telecommunications, School of Computer Science, Beijing, China

    Xiao Wang

  3. University of Illinois at Chicago, Department of Computer Science, Chicago, USA

    Philip S. Yu

Authors
  1. Chuan Shi
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  2. Xiao Wang
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  3. Philip S. Yu
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Correspondence to Chuan Shi , Xiao Wang or Philip S. Yu .

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Shi, C., Wang, X., S. Yu, P. (2022). Heterogeneous Graph Representation for Recommendation. In: Heterogeneous Graph Representation Learning and Applications. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-16-6166-2_7

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  • DOI: https://doi.org/10.1007/978-981-16-6166-2_7

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