Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation

doi:10.1109/TKDE.2022.3221949

	Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation
	Zhang, Jinghao 1,2; Zhu, Yanqiao3 ; Liu, Qiang1,2 ; Zhang, Mengqi1,2 ; Wu, Shu1,2 ; Wang, Liang1,2
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
	2023-09-01
卷号	35 期号:9 页码:9154-9167
通讯作者	Wu, Shu(shu.wu@nlpr.ia.ac.cn)
摘要	Multimedia contents are of predominance in the modern Web era. Recent years have witnessed growing research interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Most previous studies focus on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. First, only collaborative item-item relationships are implicitly modeled through high-order item-user-item co-occurrences. Considering that items are associated with rich contents in multiple modalities, we argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and assist the recommender models to comprehensively discover candidate items. Second, although previous studies consider multiple modalities, their ways of fusing multiple modalities by linear combination or concatenation is insufficient to fully capture content information of items and item relationships. To address these deficiencies, we propose a latent structure MIning with ContRastive mOdality fusion model, which we term MICRO for brevity. To be specific, we devise a novel modality-aware structure learning module, which learns item-item relationships for each modality. Based on the learned modality-aware latent item relationships, we perform graph convolutions to explicitly inject item affinities into modality-aware item representations. Additionally, we design a novel multimodal contrastive framework to facilitate item-level multimodal fusion by mining both modality-shared and modality-specific information. Finally, the item representations are plugged into existing collaborative filtering methods to make accurate recommendation. Extensive experiments on three real-world datasets demonstrate the superiority of our method over state-of-arts and rationalize the design choice of our work.
关键词	Multimedia recommendation graph structure learning contrastive learning
DOI	10.1109/TKDE.2022.3221949
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62141608] ; National Natural Science Foundation of China[62236010] ; National Natural Science Foundation of China[62206291]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001045704800034
出版者	IEEE COMPUTER SOC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53943
专题	多模态人工智能系统全国重点实验室
通讯作者	Wu, Shu
作者单位	1.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Beijing 100045, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 3.Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhang, Jinghao,Zhu, Yanqiao,Liu, Qiang,et al. Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(9):9154-9167.
APA	Zhang, Jinghao,Zhu, Yanqiao,Liu, Qiang,Zhang, Mengqi,Wu, Shu,&Wang, Liang.(2023).Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(9),9154-9167.
MLA	Zhang, Jinghao,et al."Latent Structure Mining With Contrastive Modality Fusion for Multimedia Recommendation".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.9(2023):9154-9167.