Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning approach to session-based product recommendation in e-commerce, especially for unknown user intents, by modeling the problem as an MDP and predicting product attributes to improve engagement.

Contribution

It introduces a novel DRL framework for unknown intent session recommendations, breaking down the problem into attribute prediction to handle large behavioral variance.

Findings

DRL outperforms greedy strategies in recommendations

Predicting product attributes improves model accuracy

Effective for unknown user intent scenarios

Abstract

Sustaining users' interest and keeping them engaged in the platform is very important for the success of an e-commerce business. A session encompasses different activities of a user between logging into the platform and logging out or making a purchase. User activities in a session can be classified into two groups: Known Intent and Unknown intent. Known intent activity pertains to the session where the intent of a user to browse/purchase a specific product can be easily captured. Whereas in unknown intent activity, the intent of the user is not known. For example, consider the scenario where a user enters the session to casually browse the products over the platform, similar to the window shopping experience in the offline setting. While recommending similar products is essential in the former, accurately understanding the intent and recommending interesting products is essential in the latter setting in order to retain a user. In this work, we focus primarily on the unknown intent setting where our objective is to recommend a sequence of products to a user in a session to sustain their interest, keep them engaged and possibly drive them towards purchase. We formulate this problem in the framework of the Markov Decision Process (MDP), a popular mathematical framework for sequential decision making and solve it using Deep Reinforcement Learning (DRL) techniques. However, training the next product recommendation is difficult in the RL paradigm due to large variance in browse/purchase behavior of the users. Therefore, we break the problem down into predicting various product attributes, where a pattern/trend can be identified and exploited to build accurate models. We show that the DRL agent provides better performance compared to a greedy strategy.