# A Survey on E-Commerce Learning to Rank

arXiv: https://arxiv.org/abs/2412.03581 · HTML: https://mlsanity.com/paper/2412.03581/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2024.

## Built on by (most cited first, 5 of 5)

- [GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization](https://mlsanity.com/paper/2505.13731/) (2025)
- [Extracting Important Tokens in E-Commerce Queries with a Tag Interaction-Aware Transformer Model](https://mlsanity.com/paper/2507.10385/) (2025)
- [SkyLink: A Large Vision-Language Model Driven Re-ranking Framework for Cross-View UAV geolocalization](https://mlsanity.com/paper/2603.08063/) (2026)
- [Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation](https://mlsanity.com/paper/2608.05819/) (2026)
- [EvoRank: LLM-Guided Evolution of Multi-Objective Learning-to-Rank Pipelines](https://mlsanity.com/paper/2609.22196/) (2026)

## Abstract (arXiv)

In e-commerce, ranking the search results based on users' preference is the most important task. Commercial e-commerce platforms, such as, Amazon, Alibaba, eBay, Walmart, etc. perform extensive and relentless research to perfect their search result ranking algorithms because the quality of ranking drives a user's decision to purchase or not to purchase an item, directly affecting the profitability of the e-commerce platform. In such a commercial platforms, for optimizing search result ranking numerous features are considered, which emerge from relevance, personalization, seller's reputation and paid promotion. To maintain their competitive advantage in the market, the platforms do no publish their core ranking algorithms, so it is difficult to know which of the algorithms or which of the features is the most effective for finding the most optimal search result ranking in e-commerce. No extensive surveys of ranking to rank in the e-commerce domain is also not yet published. In this work, we survey the existing e-commerce learning to rank algorithms. Besides, we also compare these algorithms based on query relevance criterion on a large real-life e-commerce dataset and provide a quantitative analysis. To the best of our knowledge this is the first such survey which include an experimental comparison among various learning to rank algorithms.