Progressive Refinement of E-commerce Search Ranking Based on Short-Term Activities of the Buyer

2025-12-15

AI Summary

To address the dynamic evolution of buyer intent in e-commerce search—leading to misalignment between search engine result pages (SERPs) and real-time user needs—this paper proposes a progressive context-aware ranking framework. The framework integrates short-term behavioral sequences (e.g., clicks, add-to-cart actions, dwell time) and employs autoregressive feature engineering, Transformer-based sequential modeling, multi-stage online learning, and context-aware re-ranking to dynamically align search ranking with users’ instantaneous intent states. Its key contribution is a novel progressive refinement paradigm, bridging heuristic autoregressive features to end-to-end sequence models. Extensive offline evaluations and online A/B tests demonstrate statistically significant improvements in Mean Reciprocal Rank (MRR), search relevance, and conversion rate, validating the framework’s effectiveness in capturing evolving user intent.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Learning Preferences or Rankings
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Adapts e-commerce search results to buyer's immediate needs and preferences
• Progressively refines ranking using contextual information and advanced techniques
• Improves search result alignment with buyer's interests and current intentions

Innovation

• Progressive refinement using evolving contextual framework
• Integration of advanced sequence models for ranking
• Iterative methodologies enhancing search result contextualization