Multi-Interest Multi-Round Conversational Recommendation System with Fuzzy Feedback Based User Simulator
Bibliographic record
Abstract
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, which often deviates from the real scenario. The user may have a clear single preference for some attribute types (e.g., brand) of items, while for other attribute types (e.g., color), the user may have multiple preferences or even no clear preferences, which leads to multiple acceptable items under multiple combinations of attribute instances. Furthermore, previous works assume that users would provide clear responses to any questions asked by the system. And, they also assume that users would be dedicated to the target item, that is, user would answer “yes” to the attribute corresponding to the target item and answer “no” to other attributes. However, users’ responses to attributes are not completely dependent on target items, but also influenced by users’ inherent interests. Besides, for some over-specific or equivocal questions, the feedback of user might not be clear (“yes”/“no”) and user might give some fuzzy response like “I don’t know”. To address the aforementioned issues, we first propose a more realistic conversational recommendation learning setting, namely Multi-Interest Multi-round Conversational Recommendation (MIMCR), 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 MIMCR, we propose a novel learning framework, namely Multiple Choice questions based on Multi-Interest Policy Learning. Moreover, we further propose a more realistic User-centric User Simulator with Fuzzy Feedback (UUSFF), which naturally calibrates the user response with additional fuzzy feedback based on user’s inherent preference. To better match the new scenario UUSFF, we propose a simple but effective adaption method for different backbones. Extensive experimental results on several datasets demonstrate the superiority of our methods for the proposed settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".