{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dualgnn-dual-graph-neural-network-for","title":"DualGNN: Dual Graph Neural Network for Multimedia Recommendation","arxiv_id":null,"date":"2021-12-24","proceeding":"IEEE Transactions on Multimedia (TMM) 2021 12","authors":["Qifan Wang","Yinwei Wei","Jianhua Yin","Jianlong Wu","Xuemeng Song","Liqiang Nie"],"abstract":"One of the important factors affecting micro-video recommender systems is to model the multi-modal user preference on the micro-video. Despite the remarkable performance of prior arts, they are still limited by fusing the user preference derived from different modalities in a unified manner, ignoring the users tend to place different emphasis on different modalities. Furthermore, modality-missing is ubiquity and unavoidable in the micro-video recommendation, some modalities information of micro-videos are lacked in many cases, which negatively affects the multi-modal fusion operations. To overcome these disadvantages, we propose a novel framework for the micro-video recommendation, dubbed Dual Graph Neural Network (DualGNN), upon the user-microvideo bipartite and user co-occurrence graphs, which leverages the correlation between users to collaboratively mine the particular fusion pattern for each user. Specifically, we first introduce a single-modal representation learning module, which performs graph operations on the user-microvideo graph in each modality to capture single-modal user preferences on different modalities. And then, we devise a multi-modal representation learning module to explicitly model the user’s attentions over different modalities and inductively learn the multi-modal user preference. Finally, we propose a prediction module to rank the potential micro-videos for users. Extensive experiments on two public datasets demonstrate the significant superiority of our DualGNN over state-of-the-arts methods.","url_abs":"https://ieeexplore.ieee.org/document/9662655","url_pdf":"https://ieeexplore.ieee.org/document/9662655","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dualgnn-dual-graph-neural-network-for","repo_url":"https://github.com/wqf321/dualgnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"multi-modal-recommendation","task_name":"Multi-modal Recommendation"},{"task_slug":"multimedia-recommendation","task_name":"Multimedia recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-modal-recommendation-on-amazon-baby","task":"Multi-modal Recommendation","dataset":"Amazon Baby","model":"DualGNN","rank_in_archive_order":6,"of":10,"metrics":{"NDCG@20":"0.0352"},"uses_additional_data":false},{"leaderboard":"/sota/multi-modal-recommendation-on-amazon-clothing","task":"Multi-modal Recommendation","dataset":"Amazon Clothing","model":"DualGNN","rank_in_archive_order":4,"of":10,"metrics":{"NDCG@20":"0.0298"},"uses_additional_data":false},{"leaderboard":"/sota/multi-modal-recommendation-on-amazon-sports","task":"Multi-modal Recommendation","dataset":"Amazon Sports","model":"DualGNN","rank_in_archive_order":7,"of":10,"metrics":{"NGCG@20":"0.0404"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}