  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 video, 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,
Multimodal Recommender System (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 three categories, i.e., Feature Interaction,
Feature Enhancement and Model Optimization. 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.
