FEW: Multi-modal Recommendation for Cold-Start

With the development of the Internet and computer technology, the phenomenon of information explosion has occurred in many fields, such as computer vision and natural language processing. People are gradually moving into the era of information overload, and it is very difficult to find effective information from the massive multi-modal data. Recommender systems are important tools to solve this difficulty. Recommendation systems currently face many challenges, one of the most important challenges in application scenarios is the cold-start problem. General recommendation systems are inaccurate for cold users/items, which have very FEW contact with the recommendation system, because of the lack of interaction data. To address the cold-start problem, this paper proposes a Four-viEW (FEW) multi-modal recommendation model, which improves the recommendation effect by constructing a four-view graph convolutional network for enhancing the multi-modal (mainly visual and textual) data representations of cold users/items. FEW is compared with existing single-modal and multi-modal recommendation models on the Amazon review datasets. Overall, compared to existing state-of-the-art models, FEW improves Recall by a maximum of 15.72% and NDCG by a maximum of 19.86%. In particular, the addition of cold-start technology further enhances the recommendation effect of FEW. For cold users, the maximum improvement is 20.86% for Recall and 27.47% for NDCG; for cold items, the average improvement of Recall is 85.14x and NDCG is 63.08x, which verifies the effectiveness of FEW in solving the cold-start problem.