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 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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".