# Dataset of Natural Language Queries for E-Commerce

arXiv: https://arxiv.org/abs/2302.06355 · HTML: https://mlsanity.com/paper/2302.06355/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2023.

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

- [Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://mlsanity.com/paper/2305.07001/) (2023)
- ["You tell me": A Dataset of GPT-4-Based Behaviour Change Support Conversations](https://mlsanity.com/paper/2401.16167/) (2024)
- [SourceBench: Can AI Answers Reference Quality Web Sources?](https://mlsanity.com/paper/2602.16942/) (2026)
- [Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts](https://mlsanity.com/paper/2606.14929/) (2026)
- [IntentTune: Using user demand and personalization to resolve "unknown" query intents for e-commerce search](https://mlsanity.com/paper/2607.01530/) (2026)

## Abstract (arXiv)

Shopping online is more and more frequent in our everyday life. For e-commerce search systems, understanding natural language coming through voice assistants, chatbots or from conversational search is an essential ability to understand what the user really wants. However, evaluation datasets with natural and detailed information needs of product-seekers which could be used for research do not exist. Due to privacy issues and competitive consequences, only few datasets with real user search queries from logs are openly available. In this paper, we present a dataset of 3,540 natural language queries in two domains that describe what users want when searching for a laptop or a jacket of their choice. The dataset contains annotations of vague terms and key facts of 1,754 laptop queries. This dataset opens up a range of research opportunities in the fields of natural language processing and (interactive) information retrieval for product search.