Prospect-Net: Top-K Retrieval Problem Using Prospect Theory

Reza Yousefi Maragheh, Ramin Giahi, Jianpeng Xu, Lalitesh Morishetti, Shanu Vashishtha, Kaushiki Nag, Jason H. D. Cho, Evren Körpeoglu, Sushant Kumar, Kannan Achan

Abstract: In e-Commerce Industry, customers’ purchase decision of an item are usually influenced by the reference price of that item, which is implied within the context of the items (e.g. prices of an item set from search/recommendation) or external environments (e.g. prices from another e-Commerce platform). Despite of the prevalence and influence of the reference price on customers’ behavior, existing works in Information Retrieval domain do not exploit the value of the reference price in ranking problems. In this paper, we propose a list-wise ranking model named "Prospect-Net" by incorporating the prospect theory, which is the theoretical foundation for framing the reference price. We consider the Top-K retrieval task under a product recommendation setting, and demonstrate the effectiveness of Prospect-Net to capture various forms of reference price under different scenarios. Polynomial solutions are proposed to solve the Top-K retrieval problem for some of the cases where the reference price is dependent on the recommended set of items to the user. Both offline e valuation and online experiments are performed on a real-world industrial dataset with significant performance improvement.