Multimedia Recommendation

Multimedia recommendation aims to personalize suggestions by leveraging diverse data types (text, images, audio, etc.) to understand user preferences more comprehensively than traditional methods. Current research focuses on improving the integration of multimodal data, addressing issues like data imbalance and spurious correlations through techniques such as graph convolutional networks, counterfactual inference, and contrastive learning. These advancements aim to enhance recommendation accuracy and robustness, particularly for long-tail items and in dynamic environments, with implications for various applications including video, music, and artwork recommendation.