  Recommendation systems have become popular and effective tools to help users
discover their interesting items by modeling the user preference and item
property based on implicit interactions (e.g., purchasing and clicking). Humans
perceive the world by processing the modality signals (e.g., audio, text and
image), which inspired researchers to build a recommender system that can
understand and interpret data from different modalities. Those models could
capture the hidden relations between different modalities and possibly recover
the complementary information which can not be captured by a uni-modal approach
and implicit interactions. The goal of this survey is to provide a
comprehensive review of the recent research efforts on the multimodal
recommendation. Specifically, it shows a clear pipeline with commonly used
techniques in each step and classifies the models by the methods used.
Additionally, a code framework has been designed that helps researchers new in
this area to understand the principles and techniques, and easily runs the SOTA
models. Our framework is located at: https://github.com/enoche/MMRec
