  Personalized recommendation serves as a ubiquitous channel for users to
discover information tailored to their interests. However, traditional
recommendation models primarily rely on unique IDs and categorical features for
user-item matching, potentially overlooking the nuanced essence of raw item
contents across multiple modalities such as text, image, audio, and video. This
underutilization of multimodal data poses a limitation to recommender systems,
especially in multimedia services like news, music, and short-video platforms.
The recent advancements in large multimodal models offer new opportunities and
challenges in developing content-aware recommender systems. This survey seeks
to provide a comprehensive exploration of the latest advancements and future
trajectories in multimodal pretraining, adaptation, and generation techniques,
as well as their applications in enhancing recommender systems. Furthermore, we
discuss current open challenges and opportunities for future research in this
dynamic domain. We believe that this survey, alongside the curated resources,
will provide valuable insights to inspire further advancements in this evolving
landscape.
