ML Anthology

Temporal Heterogeneous Interaction Graph Embedding for Next-Item Recommendation

Yugang Ji, Mingyang Yin, Yuan Fang, Hongxia Yang, Xiangwei Wang, Tianrui Jia, Chuan Shi
ECML-PKDD 2020 pp. 314-329
doi:10.1007/978-3-030-67664-3_19 /ecmlpkdd/2020/ji2020ecmlpkdd-temporal/

Abstract

In the scenario of next-item recommendation, previous methods attempt to model user preferences by capturing the evolution of sequential interactions. However, their sequential expression is often limited, without modeling complex dynamics that short-term demands can often be influenced by long-term habits. Moreover, few of them take into account the heterogeneous types of interaction between users and items. In this paper, we model such complex data as a Temporal Heterogeneous Interaction Graph (THIG) and learn both user and item embeddings on THIGs to address next-item recommendation. The main challenges involve two aspects: the complex dynamics and rich heterogeneity of interactions. We propose THIG Embedding (THIGE) which models the complex dynamics so that evolving short-term demands are guided by long-term historical habits, and leverages the rich heterogeneity to express the latent relevance of different-typed preferences. Extensive experiments on real-world datasets demonstrate that THIGE consistently outperforms the state-of-the-art methods.

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Cite

Text

Ji et al. "Temporal Heterogeneous Interaction Graph Embedding for Next-Item Recommendation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67664-3_19

Markdown

[Ji et al. "Temporal Heterogeneous Interaction Graph Embedding for Next-Item Recommendation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/ji2020ecmlpkdd-temporal/) doi:10.1007/978-3-030-67664-3_19

BibTeX

@inproceedings{ji2020ecmlpkdd-temporal,
  title     = {{Temporal Heterogeneous Interaction Graph Embedding for Next-Item Recommendation}},
  author    = {Ji, Yugang and Yin, Mingyang and Fang, Yuan and Yang, Hongxia and Wang, Xiangwei and Jia, Tianrui and Shi, Chuan},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2020},
  pages     = {314-329},
  doi       = {10.1007/978-3-030-67664-3_19},
  url       = {https://mlanthology.org/ecmlpkdd/2020/ji2020ecmlpkdd-temporal/}
}