Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application (1803.00710v3)

Abstract: In e-commerce platforms such as Amazon and TaoBao, ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widely applied to ranking problems. However, such methods often consider different ranking steps in a session to be independent, which conversely may be highly correlated to each other. For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session. Firstly, we formally define the concept of search session Markov decision process (SSMDP) to formulate the multi-step ranking problem. Secondly, we analyze the property of SSMDP and theoretically prove the necessity of maximizing accumulative rewards. Lastly, we propose a novel policy gradient algorithm for learning an optimal ranking policy, which is able to deal with the problem of high reward variance and unbalanced reward distribution of an SSMDP. Experiments are conducted in simulation and TaoBao search engine. The results demonstrate that our algorithm performs much better than online LTR methods, with more than 40% and 30% growth of total transaction amount in the simulation and the real application, respectively.

• The paper introduces a novel SSMDP formalization to capture the sequential dependencies in e-commerce search ranking.
• The paper employs a deterministic policy gradient with full backup estimation (DPG-FBE) algorithm to reduce reward variance and boost transaction growth by over 40% in simulation and 30% in real-world deployments.
• The paper demonstrates significant improvements in gross merchandise volume, showcasing practical benefits and broad applicability in AI-driven ranking systems.

Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application

The paper "Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application" introduces novel methodologies by leveraging reinforcement learning (RL) to improve the efficiency of item ranking in e-commerce search engines. This approach addresses the limitations posed by conventional learning to rank (LTR) frameworks, which often treat ranking decisions as independent actions rather than exploiting the correlations inherent in sequential ranking tasks.

This research proposes the innovative concept of a Search Session Markov Decision Process (SSMDP) to model multi-step ranking problems within e-commerce platforms such as Taobao and Amazon. By formalizing the ranking task as an SSMDP, the researchers are able to capture the dependencies between successive ranking actions and better optimize the overall ranking policy. The SSMDP incorporates the critical aspects of states representing item page histories, actions depicting ranking choices, and rewards based on successful transactions. A key component of their approach is the deterministic policy gradient with full backup estimation (DPG-FBE) algorithm, designed to mitigate issues related to the high variance and imbalance in rewards due to fluctuating transaction prices.

Experimental results within both simulated environments and real-world applications offer compelling insights. In simulation, DPG-FBE outperformed existing online learning algorithms by achieving substantial transaction growth percentages, specifically over 40% and 30% increases in simulation and actual deployment, respectively. The choice of discount factor was pivotal, as the results underscored the optimization benefits achieved by considering long-term accumulative rewards versus immediate gains. This was substantiated by the comparison to other online LTR paradigms, which are limited by single-step decision frameworks.

The implications for e-commerce platforms are significant. By integrating RL into the ranking process, these systems can possibly realize enhanced gross merchandise volumes (GMV), as evidenced by the 30% increase during a high-stakes shopping event—the TMall Double 11 Global Shopping Festival. This paper's findings suggest applications beyond traditional search engines, potentially influencing future AI-driven decision-making frameworks across various industries where sequential decision-making and transaction conversion rates are imperative.

Future developments could explore deeper integration with neural network architectures to further enhance scalability and processing efficiency amidst high-concurrency scenarios typical in major e-commerce platforms. Additionally, the exploration of stochastic policies and other sophisticated RL paradigms might offer even further optimization potential.

Overall, this research exemplifies a substantial contribution to the domain of e-commerce search engines, delivering both theoretical advancements via the SSMDP conceptualization and practical enhancements showcased through real-world implementations.