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Motifs-based Recommender System via Hypergraph Convolution and Contrastive Learning by Yundong Sun; Dongjie Zhu; Haiwen Du; Zhaoshuo Tian is a Computer Science article available to read on EtoBox.

What is Motifs-based Recommender System via Hypergraph Convolution and Contrastive Learning about?

Recently, the strategy of leveraging various motifs to model social semantic information and using self-supervised learning tasks to boost recommendation performance has been proven to be very promising. In this paradigm, each channel describes a common motif (e.g., a triangular social motif) via hypergraph convolution. Richer motifs can be encoded through multiple channels, and self-supervised learning can leverage this multichannel information to build self-supervised tasks (such as contrastive learning tasks), which can greatly improve the recommendation performance in scenarios without enough data labels. However, accurately determining the relationships between different channels and fully utilizing them, while maintaining the uniqueness of each channel, is a problem that has not been well studied or resolved in this field. This paper explores and verifies the disadvantages of directly constructing contrastive learning tasks on different channels with practical experiments and proposes a scheme of interactive modeling and matching representation across different channels. This is the first such attempt in the field of recommender systems, and we believe that this paper will in

Who reads Motifs-based Recommender System via Hypergraph Convolution and Contrastive Learning?

Author

Yundong Sun; Dongjie Zhu; Haiwen Du; Zhaoshuo Tian

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)