# Learning to Ask: Question-based Sequential Bayesian Product Search

arXiv: https://arxiv.org/abs/1908.11733 · HTML: https://mlsanity.com/paper/1908.11733/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Learning Neural Templates for Recommender Dialogue System](https://mlsanity.com/paper/2109.12302/) (2021)
- [Category Aware Explainable Conversational Recommendation](https://mlsanity.com/paper/2103.08733/) (2021)
- [PSCon: Product Search Through Conversations](https://mlsanity.com/paper/2502.13881/) (2025)
- [Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant Items](https://mlsanity.com/paper/2507.18017/) (2025)
- [EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context](https://mlsanity.com/paper/2407.04472/) (2024)
- [Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering](https://mlsanity.com/paper/2604.22759/) (2026)

## Abstract (arXiv)

Product search is generally recognized as the first and foremost stage of online shopping and thus significant for users and retailers of e-commerce. Most of the traditional retrieval methods use some similarity functions to match the user's query and the document that describes a product, either directly or in a latent vector space. However, user queries are often too general to capture the minute details of the specific product that a user is looking for. In this paper, we propose a novel interactive method to effectively locate the best matching product. The method is based on the assumption that there is a set of candidate questions for each product to be asked. In this work, we instantiate this candidate set by making the hypothesis that products can be discriminated by the entities that appear in the documents associated with them. We propose a Question-based Sequential Bayesian Product Search method, QSBPS, which directly queries users on the expected presence of entities in the relevant product documents. The method learns the product relevance as well as the reward of the potential questions to be asked to the user by being trained on the search history and purchase behavior of a specific user together with that of other users. The experimental results show that the proposed method can greatly improve the performance of product search compared to the state-of-the-art baselines.