# Identifying High Consideration E-Commerce Search Queries

arXiv: https://arxiv.org/abs/2410.13951 · HTML: https://mlsanity.com/paper/2410.13951/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Question Suggestion for Conversational Shopping Assistants Using Product Metadata](https://mlsanity.com/paper/2405.01738/) (2024)
- [Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers](https://mlsanity.com/paper/2406.05255/) (2024)
- [Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search](https://mlsanity.com/paper/2306.03411/) (2023)
- [Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce](https://mlsanity.com/paper/2407.09653/) (2024)
- [Did We Get It Right? Predicting Query Performance in E-commerce Search](https://mlsanity.com/paper/1808.00239/) (2018)
- [Community Trend Prediction on Heterogeneous Graph in E-commerce](https://mlsanity.com/paper/2202.12081/) (2022)

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

- [DeepShop: A Benchmark for Deep Research Shopping Agents](https://mlsanity.com/paper/2506.02839/) (2025)
- [We Should Evaluate Real-World Impact](https://mlsanity.com/paper/2507.05973/) (2025)
- [REIC: RAG-Enhanced Intent Classification at Scale](https://mlsanity.com/paper/2506.00210/) (2025)
- [Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decomposition](https://mlsanity.com/paper/2511.13994/) (2025)

## Abstract (arXiv)

In e-commerce, high consideration search missions typically require careful and elaborate decision making, and involve a substantial research investment from customers. We consider the task of identifying High Consideration (HC) queries. Identifying such queries enables e-commerce sites to better serve user needs using targeted experiences such as curated QA widgets that help users reach purchase decisions. We explore the task by proposing an Engagement-based Query Ranking (EQR) approach, focusing on query ranking to indicate potential engagement levels with query-related shopping knowledge content during product search. Unlike previous studies on predicting trends, EQR prioritizes query-level features related to customer behavior, finance, and catalog information rather than popularity signals. We introduce an accurate and scalable method for EQR and present experimental results demonstrating its effectiveness. Offline experiments show strong ranking performance. Human evaluation shows a precision of 96% for HC queries identified by our model. The model was commercially deployed, and shown to outperform human-selected queries in terms of downstream customer impact, as measured through engagement.