Progressive Refinement of E-commerce Search Ranking Based on Short-Term Activities of the Buyer
This incremental approach addresses the problem of adapting search results to buyer context for e-commerce platforms.
The study tackled the challenge of aligning e-commerce search results with a buyer's immediate needs by progressively refining ranking methods from basic heuristics to advanced sequence models, resulting in significant improvements in ranker performance as measured by Mean Reciprocal Rank (MRR) in offline and online A/B tests.
In e-commerce shopping, aligning search results with a buyer's immediate needs and preferences presents a significant challenge, particularly in adapting search results throughout the buyer's shopping journey as they move from the initial stages of browsing to making a purchase decision or shift from one intent to another. This study presents a systematic approach to adapting e-commerce search results based on the current context. We start with basic methods and incrementally incorporate more contextual information and state-of-the-art techniques to improve the search outcomes. By applying this evolving contextual framework to items displayed on the search engine results page (SERP), we progressively align search outcomes more closely with the buyer's interests and current search intentions. Our findings demonstrate that this incremental enhancement, from simple heuristic autoregressive features to advanced sequence models, significantly improves ranker performance. The integration of contextual techniques enhances the performance of our production ranker, leading to improved search results in both offline and online A/B testing in terms of Mean Reciprocal Rank (MRR). Overall, the paper details iterative methodologies and their substantial contributions to search result contextualization on e-commerce platforms.