# Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems arXiv: https://arxiv.org/abs/2111.13463 ยท HTML: https://mlsanity.com/paper/2111.13463/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 79 references that are in the map. - First posted on arXiv 26 Nov 2021. ## Builds on (most cited first, 30 of 34) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014) - [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018) - [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018) - [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018) - [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016) - [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017) - [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018) - [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018) - [A Survey of Evaluation Metrics Used for NLG Systems](https://mlsanity.com/paper/2008.12009/) (2020) - [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020) - [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019) - [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019) - [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021) - [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) - [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020) - [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019) - [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://mlsanity.com/paper/2101.05667/) (2021) - [Denoising Implicit Feedback for Recommendation](https://mlsanity.com/paper/2006.04153/) (2020) - [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020) - [Evaluating Conversational Recommender Systems via User Simulation](https://mlsanity.com/paper/2006.08732/) (2020) - [Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models](https://mlsanity.com/paper/2004.01909/) (2020) - [Answer-based Adversarial Training for Generating Clarification Questions](https://mlsanity.com/paper/1904.02281/) (2019) - [Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders](https://mlsanity.com/paper/1805.04843/) (2018) - [On Interpretation and Measurement of Soft Attributes for Recommendation](https://mlsanity.com/paper/2105.09179/) (2021) ## Built on by (most cited first, 10 of 10) - [Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset](https://mlsanity.com/paper/2303.06791/) (2023) - [Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers](https://mlsanity.com/paper/2406.05255/) (2024) - [Aligning Recommendation and Conversation via Dual Imitation](https://mlsanity.com/paper/2211.02848/) (2022) - [Zero-Shot and Efficient Clarification Need Prediction in Conversational Search](https://mlsanity.com/paper/2503.00179/) (2025) - [Alexpaca: Learning Factual Clarification Question Generation Without Examples](https://mlsanity.com/paper/2310.11571/) (2023) - [Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation](https://mlsanity.com/paper/2609.03727/) (2026) - [Explainable Active Learning for Preference Elicitation](https://mlsanity.com/paper/2309.00356/) (2023) - [RPS: Information Elicitation with Reinforcement Prompt Selection](https://mlsanity.com/paper/2604.13817/) (2026) - [ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing](https://mlsanity.com/paper/2606.15315/) (2026) - [Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation](https://mlsanity.com/paper/2608.06632/) (2026) ## Abstract (arXiv) A key distinguishing feature of conversational recommender systems over traditional recommender systems is their ability to elicit user preferences using natural language. Currently, the predominant approach to preference elicitation is to ask questions directly about items or item attributes. Users searching for recommendations may not have deep knowledge of the available options in a given domain. As such, they might not be aware of key attributes or desirable values for them. However, in many settings, talking about the planned use of items does not present any difficulties, even for those that are new to a domain. In this paper, we propose a novel approach to preference elicitation by asking implicit questions based on item usage. As one of the main contributions of this work, we develop a multi-stage data annotation protocol using crowdsourcing, to create a high-quality labeled training dataset. Another main contribution is the development of four models for the question generation task: two template-based baseline models and two neural text-to-text models. The template-based models use heuristically extracted common patterns found in the training data, while the neural models use the training data to learn to generate questions automatically. Using common metrics from machine translation for automatic evaluation, we show that our approaches are effective in generating elicitation questions, even with limited training data. We further employ human evaluation for comparing the generated questions using both pointwise and pairwise evaluation designs. We find that the human evaluation results are consistent with the automatic ones, allowing us to draw conclusions about the quality of the generated questions with certainty. Finally, we provide a detailed analysis of cases where the models show their limitations.