Understanding and Learning from User Behavior for Recommendation in Multi-channel Retail

Online shopping is gaining more and more popularity everyday. Traditional retailers with physical stores adjust to this trend by allowing their customers to shop online as well as offline, i.e., in-store. Increasingly, customers can browse and purchase products across multiple shopping channels. Understanding how customer behavior relates to the availability of multiple shopping channels is an important prerequisite for many downstream machine learning tasks, such as recommendation and purchase prediction. However, previous work in this domain is limited to analyzing single-channel behavior only. In this project, we first provide a better understanding of the similarities and differences between online and offline behavior. We further study the next basket recommendation task in a multi-channel context, where the goal is to build recommendation algorithms that can leverage the rich cross-channel user behavior data in order to enhance the customer experience.