Publication Type

Conference Proceeding Article

Version

submittedVersion

Publication Date

9-2020

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.

Keywords

Temporal heterogeneous interaction graph, Next-item recommendation, Short-term demands, Long-term habit

Discipline

Computer Engineering | Databases and Information Systems

Research Areas

Information Systems and Management

Publication

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD, Ghent, Belgium, September 14-18: Proceedings

Volume

12459

First Page

1

Last Page

16

ISBN

9783030676643

Identifier

10.1007/978-3-030-67664-3_19

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-030-67664-3_19

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