# Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation

arXiv: https://arxiv.org/abs/2112.11775 · HTML: https://mlsanity.com/paper/2112.11775/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2021.

## Built on by (most cited first, 20 of 20)

- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects](https://mlsanity.com/paper/2305.02750/) (2023)
- [Towards Human-centered Proactive Conversational Agents](https://mlsanity.com/paper/2404.12670/) (2024)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [TREA: Tree-Structure Reasoning Schema for Conversational Recommendation](https://mlsanity.com/paper/2307.10543/) (2023)
- [Bundle MCR: Towards Conversational Bundle Recommendation](https://mlsanity.com/paper/2207.12628/) (2022)
- [Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation](https://mlsanity.com/paper/2405.12119/) (2024)
- [Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching](https://mlsanity.com/paper/2502.00969/) (2025)
- [Towards Goal-oriented Intelligent Tutoring Systems in Online Education](https://mlsanity.com/paper/2312.10053/) (2023)
- [Meta Policy Learning for Cold-Start Conversational Recommendation](https://mlsanity.com/paper/2205.11788/) (2022)
- [Aligning Recommendation and Conversation via Dual Imitation](https://mlsanity.com/paper/2211.02848/) (2022)
- [SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search](https://mlsanity.com/paper/2410.09580/) (2024)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [LaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation](https://mlsanity.com/paper/2503.23312/) (2025)
- [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)
- [Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation](https://mlsanity.com/paper/2604.11077/) (2026)
- [Research on Conversational Recommender System Considering Consumer Types](https://mlsanity.com/paper/2508.13209/) (2025)
- [Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency](https://mlsanity.com/paper/2608.22266/) (2026)
- [FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System](https://mlsanity.com/paper/2609.20175/) (2026)

## Abstract (arXiv)

Conversational recommendation system (CRS) is able to obtain fine-grained and dynamic user preferences based on interactive dialogue. Previous CRS assumes that the user has a clear target item. However, for many users who resort to CRS, they might not have a clear idea about what they really like. Specifically, the user may have a clear single preference for some attribute types (e.g. color) of items, while for other attribute types, the user may have multiple preferences or even no clear preferences, which leads to multiple acceptable attribute instances (e.g. black and red) of one attribute type. Therefore, the users could show their preferences over items under multiple combinations of attribute instances rather than a single item with unique combination of all attribute instances. As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances. To effectively cope with the new CRS learning setting, in this paper, we propose a novel learning framework namely, Multi-Choice questions based Multi-Interest Policy Learning . In order to obtain user preferences more efficiently, the agent generates multi-choice questions rather than binary yes/no ones on specific attribute instance. Besides, we propose a union set strategy to select candidate items instead of existing intersection set strategy in order to overcome over-filtering items during the conversation. Finally, we design a Multi-Interest Policy Learning module, which utilizes captured multiple interests of the user to decide next action, either asking attribute instances or recommending items. Extensive experimental results on four datasets verify the superiority of our method for the proposed setting.