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.

中文翻译：

---

多模式推荐系统：一项调查


推荐系统 （RS） 一直是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，以根据标识符和属性信息对用户偏好进行建模。随着多媒体服务的出现，如短视频、新闻等，在推荐的同时理解这些内容变得至关重要。此外，多模态特性也有助于缓解 RS 中数据稀疏的问题。因此，M ultimodal R ecommender S ystem （MRS） 近年来引起了学术界和工业界的广泛关注。在本文中，我们将主要从技术角度对 MRS 模型进行全面调查。首先，我们总结了 MRS 的一般程序和主要挑战。然后，我们根据模态编码器、特征交互、特征增强和模型优化四类介绍了现有的 MRS 模型。此外，为了方便那些想研究这个领域的人，我们还总结了数据集和代码资源。最后，我们讨论了 MRS 的一些有前途的未来发展方向，并对本文进行了总结。为了访问所调查论文的更多详细信息，例如实现代码，我们开源了一个存储库。