The ideal conversational recommender system (CRS) acts like a savvy salesperson, adapting its language and suggestions to each user's level of expertise. However, most current systems treat all users as experts, leading to frustrating and inefficient interactions when users are unfamiliar with a domain. Systems that can adapt their conversational strategies to a user's knowledge level stand to offer a much more natural and effective experience. To make a step toward such adaptive systems, we introduce a new task: estimating user domain knowledge from conversations, enabling a CRS to better understand user needs and personalize interactions. A key obstacle to developing such adaptive systems is the lack of suitable data; to our knowledge, no existing dataset captures the conversational behaviors of users with varying levels of domain knowledge. Furthermore, in most dialogue collection protocols, users are free to express their own preferences, which tends to concentrate on popular items and well-known features, offering little insight into how novices explore or learn about unfamiliar features. To address this, we design a game-based data collection protocol that elicits varied expressions of knowledge, release the resulting dataset, and provide an initial analysis to highlight its potential for future work on user-knowledge-aware CRS.
RecQuest is a recommendation-game used for data collection; participants interacted with a guided search-and-recommendation task (example interaction below).
The dataset and supporting materials are available in the data/ folder; see data/README.md for an overview and file locations.
Descriptive statistics: number of dialogues (#Dial) and utterances (#Utt) are total counts, while values for the number of turns (#Turns), preferences (#Prefs), and recommendations (#Recs) are reported as mean ± standard deviation. Success rate (Success) is the proportion of dialogues where participants found the target item.
| Domain | #Dial | #Utt | #Turns (mean ± SD) | #Prefs (mean ± SD) | #Recs (mean ± SD) | Success |
|---|---|---|---|---|---|---|
| Bicycle | 79 | 1521 | 9.53 ± 4.71 | 10.00 ± 4.83 | 3.19 ± 1.35 | 60.8% |
| Digital Camera | 79 | 1687 | 10.35 ± 4.42 | 10.76 ± 5.23 | 3.42 ± 1.72 | 36.7% |
| Laptop | 98 | 2179 | 10.00 ± 4.86 | 10.47 ± 4.51 | 3.60 ± 1.27 | 25.5% |
| Running Shoes | 179 | 3636 | 10.07 ± 5.37 | 9.90 ± 5.54 | 3.52 ± 1.43 | 21.8% |
| Smartwatch | 80 | 1665 | 10.18 ± 3.63 | 9.61 ± 4.08 | 3.45 ± 1.19 | 43.8% |
| Total | 515 | 10688 | 10.21 ± 4.81 | 10.10 ± 5.03 | 3.46 ± 1.41 | 34.2% |
The crs/ directory contains a modular conversational recommender system built with LangChain and Streamlit. See crs/README.md for detailed architecture and setup instructions.
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Install dependencies:
pip install -r requirements.txt
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Set environment variables:
export OPENAI_API_KEY="your-api-key"
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Run the application:
streamlit run crs/main.py
- OpenAI (default: gpt-4.1-mini)
- Ollama (local deployment)
Should you have any questions, please contact Ivica Kostric at ivica.kostric[AT]uis.no (with [AT] replaced by @).