Similarity Learning for Product Recommendation and Scoring Using Multi-channel Data

Customers may interact with a retail store through many channels. Technology now makes it is possible to track customer behavior across channels. We propose a system where items are recommended based on learning channel specific similarities between customers and items. This is done by treating recommendations as a learning to rank problem and minimizing rank loss with surrogate loss functions. We build our system using a real world multi-channel data set -- online browse and purchase, and in-store purchase -- from a retail chain. The results show that using learned similarity scores improves the performance of the system over scores generated using standard cosine similarity measures. Finally, using our learning to rank formulation we introduce a product scoring system to measure consumption behavior.