Warm Recommendation: Enhancing Cold Start Recommendations Using Multimodal Product Representations

In the domain of online recommendation systems, the cold-start problem presents a persistent challenge, particularly acute within the fashion industry with rapid product turnover. Our study introduces a novel two-phase solution to generate recommendations when no prior user-item interactions exist. The first phase generates new item embeddings based on images, text descriptions, and attributes, then identifies the most similar existing item. The second phase utilizes a pre-established item-network to find items frequently purchased with the identified similar item. Our approach also enhances collaborative filtering when user history is available. Preliminary results, based on data from H&M's store, indicate our method's enhanced performance, with multimodal embeddings, outperforming individual modalities. Furthermore, incorporating our method into a collaborative-filtering algorithm yielded a relative improvement of 7% in hit-rate in an item cold-start scenario. This approach does not require retraining for new items or users, thus offering a promising solution to e-commerce's prevalent cold-start issue.