Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems
Bibliographic record
Abstract
Recommender systems are used to suggest items to users based on the users’ preferences. Such systems often deal with massive item sets and incredibly sparse user-item interactions, which makes it very challenging to generate high-quality personalized recommendations. Reinforcement learning (RL) is a framework for sequential decision making and naturally formulates recommender-system tasks: recommending items as actions in different user and context states to maximize long-term user experience. We investigate two RL policy parameterizations that generalize sparse user-items interactions by leveraging the relationships between actions: parameterizing the policy over action features as a softmax or Gaussian distribution. Our experiments on synthetic problems suggest that the Gaussian parameterization—which is not commonly used on recommendation tasks—is more robust to the set of action features than the softmax parameterization. Based on these promising results, we propose a more thorough investigation of the theoretical properties and empirical benefits of the Gaussian parameterization for recommender systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".