# Learning to Ask: Conversational Product Search via Representation Learning

arXiv: https://arxiv.org/abs/2411.14466 · HTML: https://mlsanity.com/paper/2411.14466/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 92 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 30 of 36)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018)
- [ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)](https://mlsanity.com/paper/2009.11352/) (2020)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [Global-Locally Self-Attentive Dialogue State Tracker](https://mlsanity.com/paper/1805.09655/) (2018)
- [Analyzing and Characterizing User Intent in Information-seeking Conversations](https://mlsanity.com/paper/1804.08759/) (2018)
- [Conversational Contextual Bandit: Algorithm and Application](https://mlsanity.com/paper/1906.01219/) (2019)
- [Learning to Ask Appropriate Questions in Conversational Recommendation](https://mlsanity.com/paper/2105.04774/) (2021)
- [Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application](https://mlsanity.com/paper/1803.00710/) (2018)
- [Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users](https://mlsanity.com/paper/2005.12979/) (2020)
- [MIMICS: A Large-Scale Data Collection for Search Clarification](https://mlsanity.com/paper/2006.10174/) (2020)
- [Towards Question-based Recommender Systems](https://mlsanity.com/paper/2005.14255/) (2020)
- [On Application of Learning to Rank for E-Commerce Search](https://mlsanity.com/paper/1903.04263/) (2019)
- [Analyzing and Learning from User Interactions for Search Clarification](https://mlsanity.com/paper/2006.00166/) (2020)
- [Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search](https://mlsanity.com/paper/2006.07548/) (2020)
- [A Zero Attention Model for Personalized Product Search](https://mlsanity.com/paper/1908.11322/) (2019)
- [Learning Latent Vector Spaces for Product Search](https://mlsanity.com/paper/1608.07253/) (2016)
- [Attentive Long Short-Term Preference Modeling for Personalized Product Search](https://mlsanity.com/paper/1811.10155/) (2018)
- [Conversational Product Search Based on Negative Feedback](https://mlsanity.com/paper/1909.02071/) (2019)
- [Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search](https://mlsanity.com/paper/2008.03717/) (2020)
- [Neural IR Meets Graph Embedding: A Ranking Model for Product Search](https://mlsanity.com/paper/1901.08286/) (2019)
- [Wizard of Search Engine: Access to Information Through Conversations with Search Engines](https://mlsanity.com/paper/2105.08301/) (2021)
- [Bias in Conversational Search: The Double-Edged Sword of the Personalized Knowledge Graph](https://mlsanity.com/paper/2010.10409/) (2020)
- [Diverse and Specific Clarification Question Generation with Keywords](https://mlsanity.com/paper/2104.10317/) (2021)

## Built on by (most cited first, 7 of 7)

- [Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching](https://mlsanity.com/paper/2502.00969/) (2025)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [Shopping Companion: Benchmarking and Training LLM Agents for Long-Horizon Preference-Grounded E-Commerce Tasks](https://mlsanity.com/paper/2603.14864/) (2026)
- [PSCon: Product Search Through Conversations](https://mlsanity.com/paper/2502.13881/) (2025)
- [Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts](https://mlsanity.com/paper/2504.13655/) (2025)
- [Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering](https://mlsanity.com/paper/2604.22759/) (2026)
- [Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants](https://mlsanity.com/paper/2606.24194/) (2026)

## Abstract (arXiv)

Online shopping platforms, such as Amazon and AliExpress, are increasingly prevalent in society, helping customers purchase products conveniently. With recent progress in natural language processing, researchers and practitioners shift their focus from traditional product search to conversational product search. Conversational product search enables user-machine conversations and through them collects explicit user feedback that allows to actively clarify the users' product preferences. Therefore, prospective research on an intelligent shopping assistant via conversations is indispensable. Existing publications on conversational product search either model conversations independently from users, queries, and products or lead to a vocabulary mismatch. In this work, we propose a new conversational product search model, ConvPS, to assist users in locating desirable items. The model is first trained to jointly learn the semantic representations of user, query, item, and conversation via a unified generative framework. After learning these representations, they are integrated to retrieve the target items in the latent semantic space. Meanwhile, we propose a set of greedy and explore-exploit strategies to learn to ask the user a sequence of high-performance questions for conversations. Our proposed ConvPS model can naturally integrate the representation learning of the user, query, item, and conversation into a unified generative framework, which provides a promising avenue for constructing accurate and robust conversational product search systems that are flexible and adaptive. Experimental results demonstrate that our ConvPS model significantly outperforms state-of-the-art baselines.