# Personalized Ranking in eCommerce Search

arXiv: https://arxiv.org/abs/1905.00052 · HTML: https://mlsanity.com/paper/1905.00052/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads](https://mlsanity.com/paper/2302.01255/) (2023)
- [TPDR: A Novel Two-Step Transformer-based Product and Class Description Match and Retrieval Method](https://mlsanity.com/paper/2310.03491/) (2023)
- [Bridging Personalization and Control in Scientific Personalized Search](https://mlsanity.com/paper/2411.02790/) (2024)
- [Progressive Refinement of E-commerce Search Ranking Based on Short-Term Activities of the Buyer](https://mlsanity.com/paper/2512.13037/) (2025)

## Abstract (arXiv)

We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a generic ranker optimized for conversion and relevance. We use a combination of latent features learned from item co-clicks in historic sessions and content-based features that use item title and price. Personalization in search has been discussed extensively in the existing literature. The novelty of our work is combining and comparing content-based and content-agnostic features and showing that they complement each other to result in a significant improvement of the ranker. Moreover, our technique does not require an explicit re-ranking step, does not rely on learning user profiles from long term search behavior, and does not involve complex modeling of query-item-user features. Our approach captures item co-click propensity using lightweight item embeddings. We experimentally show that our technique significantly outperforms a generic ranker in terms of Mean Reciprocal Rank (MRR). We also provide anecdotal evidence for the semantic similarity captured by the item embeddings on the eBay search engine.