Mining Latent Structures for Multimedia Recommendation | |
Zhang, Jinghao2,3; Zhu, Yanqiao2,3; Liu, Qiang2,3; Wu, Shu2,3,4; Wang, Shuhui1; Wang, Liang2,3 | |
2021-10 | |
会议日期 | 2021.10.20-2021.10.24 |
会议地点 | Chengdu, China |
英文摘要 | Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous work focuses on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Specifically, only collaborative item-item relationships are implicitly modeled through high-order item-user-item relations. 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 further boosting recommendation. To this end, we propose a LATent sTructure mining method for multImodal reCommEndation, which we term LATTICE for brevity. To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs. Based on the learned latent graphs, we perform graph convolutions to explicitly inject high-order item affinities into item representations. These enriched item representations can then be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over state-of-the-art multimedia recommendation methods and validate the efficacy of mining latent item-item relationships from multimodal features. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/47490] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Liu, Qiang |
作者单位 | 1.Institute of Computing Technology, Chinese Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences 3.University of Chinese Academy of Sciences 4.Artificial Intelligence Research, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Zhang, Jinghao,Zhu, Yanqiao,Liu, Qiang,et al. Mining Latent Structures for Multimedia Recommendation[C]. 见:. Chengdu, China. 2021.10.20-2021.10.24. |
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