Multi-view Hypergraph-based Contrastive Learning Model for Cold-Start Micro-video Recommendation

TL;DR

This paper proposes MHCR, a multi-view hypergraph contrastive learning model that enhances cold-start micro-video recommendation by capturing diverse interaction signals and utilizing self-supervised learning, outperforming existing methods.

Contribution

Introduces a novel multi-view hypergraph contrastive learning framework that effectively addresses cold-start issues in micro-video recommendation systems.

Findings

Significantly outperforms existing models on real-world datasets.

Effectively mitigates cold-start challenges with sparse interaction data.

Utilizes multi-view self-supervised tasks to enhance feature learning.

Abstract

With the widespread use of mobile devices and the rapid growth of micro-video platforms such as TikTok and Kwai, the demand for personalized micro-video recommendation systems has significantly increased. Micro-videos typically contain diverse information, such as textual metadata, visual cues (e.g., cover images), and dynamic video content, significantly affecting user interaction and engagement patterns. However, most existing approaches often suffer from the problem of over-smoothing, which limits their ability to capture comprehensive interaction information effectively. Additionally, cold-start scenarios present ongoing challenges due to sparse interaction data and the underutilization of available interaction signals. To address these issues, we propose a Multi-view Hypergraph-based Contrastive learning model for cold-start micro-video Recommendation (MHCR). MHCR introduces a multi-view multimodal feature extraction layer to capture interaction signals from various perspectives and incorporates multi-view self-supervised learning tasks to provide additional supervisory signals. Through extensive experiments on two real-world datasets, we show that MHCR significantly outperforms existing video recommendation models and effectively mitigates cold-start challenges. Our code is available at https://github.com/sisuolv/MHCR.