Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems
Bibliographic record
Abstract
Critiquing is a method for conversational recommendation that incrementally adapts recommendations in response to user preference feedback. Recent advances in critiquing have leveraged the power of VAE-CF recommendation in a critiquable-explainable (CE-VAE) framework that updates latent user preference embeddings based on their critiques of keyphrase-based explanations. However, the CE-VAE has two key drawbacks: (i) it uses a second VAE head to facilitate explanations and critiquing, which can sacrifice recommendation performance of the first VAE head due to multiobjective training, and (ii) it requires iterating an inverse decoding-encoding loop for multi-step critiquing that yields poor performance. To address these deficiencies, we propose a novel Bayesian Keyphrase critiquing VAE (BK-VAE) framework that builds on the strengths of VAE-CF, but avoids the problematic second head of CE-VAE. Instead, the BK-VAE uses a Concept Activation Vector (CAV) inspired approach to determine the alignment of item keyphrase properties with latent user preferences in VAE-CF. BK-VAE leverages this alignment in a Bayesian framework to model uncertainty in a user's latent preferences and to perform posterior updates to these preference beliefs after each critique --- essentially achieving CE-VAE's explanation and critique inversion through a simple application of Bayes rule. Our empirical evaluation on two datasets demonstrates that BK-VAE matches or dominates CE-VAE in both recommendation and multi-step critiquing performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".