Personalised ranking with single source implicit information for recommendation tasks a similarity based Monte Carlo Bayesian Personalised Ranking
Bibliographic record
Abstract
Background: A recommender algorithm’s main goal is to learn user preferences from the user-system interactions and provide a list of relevant items to the user. In information retrieval literature this problem is formulated as learning to rank (LtR) problem. Bayesian Personalized Ranking (BPR) [1] is one of the popular LtR approaches based on pair-wise comparison using single source implicit information. Aim: In this work, we aim to design a recommender system algorithm that generates accurate recommendations. The system should only use a single source implicit user preference information. This is possible through a good approximation of the posterior probability in BPR optimization function. Method: We proposed a Similarity based Monte Carlo approximate solution for the posterior probability in BPR. We used four datasets from different recommendation application domains to evaluate the performance of our proposed algorithm. The input data was pre-processed to match with the requirements of the algorithm. Result: The result of the analysis shows a significant improvement in terms of mean average precision (MAP) for our proposed algorithm compared with the BPR and another alternative extension to BPR. Conclusion: We conclude that the proposed approximate solution is successful in providing the most informative samples to approximate BPR posterior probability. This is confirmed by the significant improvement of the accuracy of the provided ranked list of items for the users. i
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".