{"title":"Multi-view Hypergraph-based Contrastive Learning Model for Cold-Start Micro-video Recommendation","authors":"Sisuo Lyu, Xiuze Zhou, Xuming Hu","doi":"arxiv-2409.09638","DOIUrl":null,"url":null,"abstract":"With the widespread use of mobile devices and the rapid growth of micro-video\nplatforms such as TikTok and Kwai, the demand for personalized micro-video\nrecommendation systems has significantly increased. Micro-videos typically\ncontain diverse information, such as textual metadata, visual cues (e.g., cover\nimages), and dynamic video content, significantly affecting user interaction\nand engagement patterns. However, most existing approaches often suffer from\nthe problem of over-smoothing, which limits their ability to capture\ncomprehensive interaction information effectively. Additionally, cold-start\nscenarios present ongoing challenges due to sparse interaction data and the\nunderutilization of available interaction signals. To address these issues, we propose a Multi-view Hypergraph-based Contrastive\nlearning model for cold-start micro-video Recommendation (MHCR). MHCR\nintroduces a multi-view multimodal feature extraction layer to capture\ninteraction signals from various perspectives and incorporates multi-view\nself-supervised learning tasks to provide additional supervisory signals.\nThrough extensive experiments on two real-world datasets, we show that MHCR\nsignificantly outperforms existing video recommendation models and effectively\nmitigates cold-start challenges. Our code is available at\nhttps://anonymous.4open.science/r/MHCR-02EF.","PeriodicalId":501480,"journal":{"name":"arXiv - CS - Multimedia","volume":"14 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.09638","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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://anonymous.4open.science/r/MHCR-02EF.