Integrating Matrix Factorization with Fair Re-Ranking for Improved Personalization in Recommender Systems
Bibliographic record
Abstract
Recommender systems play an essential role in simplifying personalized content recommendations and providing users with suggestions tailored to their preferences and behaviors. Despite their widespread use, recommender systems are not without challenges. These challenges are related to issues of fairness, prejudice or bias and high consumption of resources that often cause these systems problems. This research presents a comprehensive review on enhancing recommender systems using hybrid methods and integrating them with matrix factorization techniques. This study introduces a new hybrid approach that attempts to ameliorate concerns about fairness and directionality in recommendation results. To improve the overall recommendation process, this study uses the matrix factorization technique, which provides a detailed understanding of user-item interactions. The purpose of this integration is to increase fairness in the fairness optimization model. This study is investigated on three diverse datasets MovieLens, Epinions and Gowalla, which provides a multifaceted evaluation of the proposed methods. This research includes the implementation of three methods to investigate fairness and orientation, which is placed in the framework of a fairness model, and this model is combined with matrix factorization. The introduction of matrix factorization in these methods aims to improve the recommendation process, understand user preferences and item characteristics. This innovation attempts to eliminate biases and ensure a more even distribution of recommendations across user groups. In general, the amount of fairness that has been done by the fairness optimization model, the fairness evaluation values show that the fairness in the first data set increased by 6%, in the second data set by 7% and in the third data set by 5%. has done as a result, the integration of matrix factorization techniques, along with fairness considerations and re-ranking strategies, shows significant improvements in the performance of recommender systems.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".