Improving Recommendation with Self-Supervised Learning of Multimodal Embeddings

This article discusses a new approach to recommendation systems that leverages advances in natural language processing (NLP) and computer vision (CV) to improve the accuracy of recommendations. The proposed method, called CARCA, combines NLP and CV techniques to extract multi-modal item representations from textual and visual data. These representations are then used to train a recommendation system that can handle cold-start settings, where only limited information is available about the user or items.
The authors propose a novel way of combining NLP and CV representations by using a cross-attention mechanism that allows the model to focus on the most relevant information from both modalities. This approach enables the model to capture complex contextual relationships between items and users, leading to improved recommendation accuracy.
To evaluate their method, the authors conduct experiments on several real-world datasets, including Kwai and Bili datasets used in previous works. The results show that CARCA outperforms state-of-the-art baselines in both hot-start and cold-start settings, demonstrating its effectiveness in handling limited user interaction data.
The authors also analyze the contributions of different components of their method, providing insights into how each component contributes to the overall recommendation performance. They find that the cross-attention mechanism is the most critical component for improving recommendation accuracy.
In summary, this article introduces a novel approach to recommendation systems that leverages advances in NLP and CV to improve the accuracy of recommendations, particularly in cold-start settings. The proposed method combines these modalities using a cross-attention mechanism, leading to improved performance compared to state-of-the-art baselines.