MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video

Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat Seng Chua

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

251 Citations (Scopus)

Abstract

Personalized recommendation plays a central role in many online content sharing platforms. To provide quality micro-video recommendation service, it is of crucial importance to consider the interactions between users and items (i.e., micro-videos) as well as the item contents from various modalities (e.g., visual, acoustic, and textual). Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities. In this paper, we propose to exploit user-item interactions to guide the representation learning in each modality, and further personalized micro-video recommendation. We design a Multimodal Graph Convolution Network (MMGCN) framework built upon the message-passing idea of graph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences. Specifically, we construct a user-item bipartite graph in each modality, and enrich the representation of each node with the topological structure and features of its neighbors. Through extensive experiments on three publicly available datasets, Tiktok, Kwai, and MovieLens, we demonstrate that our proposed model is able to significantly outperform state-of-the-art multi-modal recommendation methods.

Original languageEnglish
Title of host publicationProceedings of the 27th ACM International Conference on Multimedia
EditorsGuillaume Gravier, Hayley Hung, Chong-Wah Ngo, Wei Tsang Ooi
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1437-1445
Number of pages9
ISBN (Electronic)9781450368896, 9781450367936
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventACM International Conference on Multimedia 2019 - Nice, France
Duration: 21 Oct 201925 Oct 2019
Conference number: 27th
https://dl.acm.org/doi/proceedings/10.1145/3343031

Conference

ConferenceACM International Conference on Multimedia 2019
Abbreviated titleMM 2019
Country/TerritoryFrance
CityNice
Period21/10/1925/10/19
Internet address

Keywords

  • Graph Convolution Network
  • Micro-video Understanding
  • Multi-modal Recommendation

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