# Analyzing and Learning from User Interactions for Search Clarification

arXiv: https://arxiv.org/abs/2006.00166 · HTML: https://mlsanity.com/paper/2006.00166/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 23 of 23)

- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots](https://mlsanity.com/paper/2406.01633/) (2024)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation](https://mlsanity.com/paper/2405.03085/) (2024)
- [Analysing Mixed Initiatives and Search Strategies during Conversational Search](https://mlsanity.com/paper/2109.05955/) (2021)
- [Evaluating Generative Ad Hoc Information Retrieval](https://mlsanity.com/paper/2311.04694/) (2023)
- [Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search](https://mlsanity.com/paper/2402.07742/) (2024)
- [AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs](https://mlsanity.com/paper/2410.19692/) (2024)
- [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024)
- [Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search](https://mlsanity.com/paper/2401.11463/) (2024)
- [Keyword Extraction for Improved Document Retrieval in Conversational Search](https://mlsanity.com/paper/2109.05979/) (2021)
- [Sherlock Your Queries: Learning to Ask the Right Questions for Dialogue-Based Retrieval](https://mlsanity.com/paper/2510.18659/) (2025)
- [When Search Agents Should Ask: DiscoBench for Clarification-Aware Deep Search](https://mlsanity.com/paper/2606.27669/) (2026)
- [Distilling Transformers for Neural Cross-Domain Search](https://mlsanity.com/paper/2108.03322/) (2021)
- [Multi-stage Clarification in Conversational AI: The case of Question-Answering Dialogue Systems](https://mlsanity.com/paper/2110.15235/) (2021)
- [Unified Interactive Multimodal Moment Retrieval via Cascaded Embedding-Reranking and Temporal-Aware Score Fusion](https://mlsanity.com/paper/2512.12935/) (2025)
- [DETOUR: An Interactive Benchmark for Dual-Agent Search and Reasoning](https://mlsanity.com/paper/2602.00352/) (2026)
- [Do Images Clarify? A Study on the Effect of Images on Clarifying Questions in Conversational Search](https://mlsanity.com/paper/2602.08700/) (2026)
- [AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation](https://mlsanity.com/paper/2603.19710/) (2026)
- [CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstruction](https://mlsanity.com/paper/2603.23989/) (2026)
- [When to Ask a Question: Understanding Communication Strategies in Generative AI Tools](https://mlsanity.com/paper/2605.11240/) (2026)
- [MapSatisfyBench: Benchmarking Satisfaction-Aware Map Agents through Behavior-Grounded Implicit Decision Factors](https://mlsanity.com/paper/2606.17453/) (2026)
- [Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency](https://mlsanity.com/paper/2608.22266/) (2026)

## Abstract (arXiv)

Asking clarifying questions in response to search queries has been recognized as a useful technique for revealing the underlying intent of the query. Clarification has applications in retrieval systems with different interfaces, from the traditional web search interfaces to the limited bandwidth interfaces as in speech-only and small screen devices. Generation and evaluation of clarifying questions have been recently studied in the literature. However, user interaction with clarifying questions is relatively unexplored. In this paper, we conduct a comprehensive study by analyzing large-scale user interactions with clarifying questions in a major web search engine. In more detail, we analyze the user engagements received by clarifying questions based on different properties of search queries, clarifying questions, and their candidate answers. We further study click bias in the data, and show that even though reading clarifying questions and candidate answers does not take significant efforts, there still exist some position and presentation biases in the data. We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback. The model is used for re-ranking a number of automatically generated clarifying questions for a given query. Evaluation on both click data and human labeled data demonstrates the high quality of the proposed method.