  There is a rapidly-growing research interest in engaging users with
multi-modal data for accurate user modeling on recommender systems. Existing
multimedia recommenders have achieved substantial improvements by incorporating
various modalities and devising delicate modules. However, when users decide to
interact with items, most of them do not fully read the content of all
modalities. We refer to modalities that directly cause users' behaviors as
point-of-interests, which are important aspects to capture users' interests. In
contrast, modalities that do not cause users' behaviors are potential noises
and might mislead the learning of a recommendation model. Not surprisingly,
little research in the literature has been devoted to denoising such potential
noises due to the inaccessibility of users' explicit feedback on their
point-of-interests. To bridge the gap, we propose a weakly-supervised framework
based on contrastive learning for denoising multi-modal recommenders (dubbed
Demure). In a weakly-supervised manner, Demure circumvents the requirement of
users' explicit feedback and identifies the noises by analyzing the modalities
of all interacted items from a given user.
