Please use this identifier to cite or link to this item: https://doi.org/10.1145/3343031.3351034
Title: MMGCN: Multimodal Graph Convolution Network for Personalized Recommendation of Micro-video
Authors: Yinwei Wei
Xiang Wang 
Liqiang Nie
Xiangnan He
Richang Hong
Tat-Seng Chua 
Keywords: Graph Convolution Network
Micro-video Understanding
Multi-modal Recommendation
Issue Date: 21-Oct-2019
Citation: Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, Tat-Seng Chua (2019-10-21). MMGCN: Multimodal Graph Convolution Network for Personalized Recommendation of Micro-video. ACM MM 2019 : 1437-1445. ScholarBank@NUS Repository. https://doi.org/10.1145/3343031.3351034
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. ? 2019 Association for Computing Machinery.
Source Title: ACM MM 2019
URI: https://scholarbank.nus.edu.sg/handle/10635/167770
ISBN: 9781450368896
DOI: 10.1145/3343031.3351034
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