  The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube)
is powering personalized recommender systems to incorporate various modalities
(eg, visual, textual and acoustic) into the latent user representations. While
existing works on multi-modal recommendation exploit multimedia content
features in enhancing item embeddings, their model representation capability is
limited by heavy label reliance and weak robustness on sparse user behavior
data. Inspired by the recent progress of self-supervised learning in
alleviating label scarcity issue, we explore deriving self-supervision signals
with effectively learning of modality-aware user preference and cross-modal
dependencies. To this end, we propose a new Multi-Modal Self-Supervised
Learning (MMSSL) method which tackles two key challenges. Specifically, to
characterize the inter-dependency between the user-item collaborative view and
item multi-modal semantic view, we design a modality-aware interactive
structure learning paradigm via adversarial perturbations for data
augmentation. In addition, to capture the effects that user's modality-aware
interaction pattern would interweave with each other, a cross-modal contrastive
learning approach is introduced to jointly preserve the inter-modal semantic
commonality and user preference diversity. Experiments on real-world datasets
verify the superiority of our method in offering great potential for multimedia
recommendation over various state-of-the-art baselines. The implementation is
released at: https://github.com/HKUDS/MMSSL.
