Multimodal Recommender Systems: A Survey

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2024-09-10 DOI:10.1145/3695461

Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang

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引用次数: 0

Abstract

The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emergence of multimedia services, such as short videos, news and etc. , understanding these contents while recommending becomes critical. Besides, multimodal features are also helpful in alleviating the problem of data sparsity in RS. Thus, M ultimodal R ecommender S ystem (MRS) has attracted much attention from both academia and industry recently. In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views. First, we conclude the general procedures and major challenges for MRS. Then, we introduce the existing MRS models according to four categories, i.e., Modality Encoder , Feature Interaction , Feature Enhancement and Model Optimization . Besides, to make it convenient for those who want to research this field, we also summarize the dataset and code resources. Finally, we discuss some promising future directions of MRS and conclude this paper. To access more details of the surveyed papers, such as implementation code, we open source a repository.

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多模式推荐系统：调查

推荐系统（RS）已成为在线服务不可或缺的工具包。它们配备了各种深度学习技术，可根据标识符和属性信息为用户偏好建模。随着短视频、新闻等多媒体服务的出现，在推荐时理解这些内容变得至关重要。随着短视频、新闻等多媒体服务的出现，在推荐时理解这些内容变得至关重要。此外，多模态特征还有助于缓解 RS 中数据稀疏的问题。因此，多模态推荐系统（Multimodal R ecommender S ystem，MRS）最近引起了学术界和产业界的广泛关注。本文将主要从技术角度对 MRS 模型进行全面考察。首先，我们总结了 MRS 的一般程序和主要挑战。然后，我们按照模态编码器、特征交互、特征增强和模型优化四个类别介绍了现有的 MRS 模型。此外，为了方便有志于该领域研究的人员，我们还总结了数据集和代码资源。最后，我们讨论了 MRS 未来的一些发展方向，并对本文进行了总结。为了获取更多调查论文的细节，如实现代码，我们开放了一个源代码库。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.