Design and implementation of intelligent library personalized information recommendation model based on reinforcement learning
Bibliographic record
Abstract
The undifferentiated recommendations in current library management systems fail to meet the diverse and personalized needs of users, and the vast amounts of user data accumulated over the years remain largely untapped. This paper integrates personalized recommendation requirements in self-service libraries with K-means clustering to design a labeling system and set user profile weights. Building on traditional reinforcement learning, we propose an Actor–Critic based recommendation algorithm that models the library recommendation task as a Markov decision process to automatically learn an optimal strategy by maximizing expected long-term rewards. The DDPG algorithm is employed to train the parameters of this framework, achieving improved personalized performance. Comparative experiments on datasets (ML-100k, Yahoo! Music, ML-1M, and Jester) demonstrate that our model outperforms traditional methods and DeepFM, with scores of 0.7708, 0.1918, 0.7155, and 0.3936, respectively. This study provides innovative insights for accurate recommendations and enhanced user experience in libraries.
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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 | 0.000 |
| 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".