# On Application of Learning to Rank for E-Commerce Search

arXiv: https://arxiv.org/abs/1903.04263 · HTML: https://mlsanity.com/paper/1903.04263/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 24 of 24)

- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [Multi-Label Learning to Rank through Multi-Objective Optimization](https://mlsanity.com/paper/2207.03060/) (2022)
- [RankFormer: Listwise Learning-to-Rank Using Listwide Labels](https://mlsanity.com/paper/2306.05808/) (2023)
- [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024)
- [Multi-Objective Personalized Product Retrieval in Taobao Search](https://mlsanity.com/paper/2210.04170/) (2022)
- [Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention](https://mlsanity.com/paper/2105.10019/) (2021)
- [Unified Embedding Based Personalized Retrieval in Etsy Search](https://mlsanity.com/paper/2306.04833/) (2023)
- [TPDR: A Novel Two-Step Transformer-based Product and Class Description Match and Retrieval Method](https://mlsanity.com/paper/2310.03491/) (2023)
- [Let the Agent Steer: Closed-Loop Ranking Optimization via Influence Exchange](https://mlsanity.com/paper/2603.27765/) (2026)
- [Semantic Retrieval at Walmart](https://mlsanity.com/paper/2412.04637/) (2024)
- [Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation](https://mlsanity.com/paper/2306.12165/) (2023)
- [Introducing "Forecast Utterance" for Conversational Data Science](https://mlsanity.com/paper/2309.03877/) (2023)
- [SHE: Stepwise Hybrid Examination Reinforcement Learning Framework for E-commerce Search Relevance](https://mlsanity.com/paper/2510.07972/) (2025)
- [Optimizing Storage Overhead of User Behavior Log for ML-embedded Mobile Apps](https://mlsanity.com/paper/2510.13405/) (2025)
- [Aggregated Customer Engagement Model](https://mlsanity.com/paper/2108.07872/) (2021)
- [Semantic Search Evaluation](https://mlsanity.com/paper/2410.21549/) (2024)
- [MOHPER: Multi-objective Hyperparameter Optimization Framework for E-commerce Retrieval System](https://mlsanity.com/paper/2503.05227/) (2025)
- [Industry Insights from Comparing Deep Learning and GBDT Models for E-Commerce Learning-to-Rank](https://mlsanity.com/paper/2507.20753/) (2025)
- [Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment](https://mlsanity.com/paper/2602.01023/) (2026)
- [Optimizing Feature Extraction for On-device Model Inference with User Behavior Sequences](https://mlsanity.com/paper/2603.21508/) (2026)
- [PreGress: Ranking-Native Pre-training and Prompting for Graph Node Ranking](https://mlsanity.com/paper/2608.09016/) (2026)
- [DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search](https://mlsanity.com/paper/2608.25635/) (2026)
- [Beyond Relevance: Structured Semantic Supervision for Product Search with LLM-Augmented Annotations](https://mlsanity.com/paper/2609.23646/) (2026)

## Abstract (arXiv)

E-Commerce (E-Com) search is an emerging important new application of information retrieval. Learning to Rank (LETOR) is a general effective strategy for optimizing search engines, and is thus also a key technology for E-Com search. While the use of LETOR for web search has been well studied, its use for E-Com search has not yet been well explored. In this paper, we discuss the practical challenges in applying learning to rank methods to E-Com search, including the challenges in feature representation, obtaining reliable relevance judgments, and optimally exploiting multiple user feedback signals such as click rates, add-to-cart ratios, order rates, and revenue. We study these new challenges using experiments on industry data sets and report several interesting findings that can provide guidance on how to optimally apply LETOR to E-Com search: First, popularity-based features defined solely on product items are very useful and LETOR methods were able to effectively optimize their combination with relevance-based features. Second, query attribute sparsity raises challenges for LETOR, and selecting features to reduce/avoid sparsity is beneficial. Third, while crowdsourcing is often useful for obtaining relevance judgments for Web search, it does not work as well for E-Com search due to difficulty in eliciting sufficiently fine grained relevance judgments. Finally, among the multiple feedback signals, the order rate is found to be the most robust training objective, followed by click rate, while add-to-cart ratio seems least robust, suggesting that an effective practical strategy may be to initially use click rates for training and gradually shift to using order rates as they become available.