# Personalized Ranking in eCommerce Search

## TL;DR

This paper presents a personalized ranking method for eCommerce search that combines content-based and co-click features, improving search relevance without complex user profiling or re-ranking.

## Contribution

It introduces a novel combination of content-based and co-click features for personalization, demonstrating significant improvements in search ranking performance.

## Key findings

- Significant improvement in Mean Reciprocal Rank (MRR) over generic rankers.
- Effective use of lightweight item embeddings to capture co-click propensity.
- Complementary benefits of combining content-based and content-agnostic features.

## Abstract

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.