Improving Diversity and Efficiency in Content-based Recommender Systems: A Genetic Algorithm Approach
Bibliographic record
Abstract
In the contemporary digital landscape, individuals are often overwhelmed by the plethora of choices available, including articles, the focus of this article. This paper explores recommender systems aimed at enhancing diversity and mitigating the homogeneity often found in traditional systems to help reduce the risk of filter bubbles and stereotypes, ensuring fair and inclusive user experiences and with little cold start problem as an item-based system. Compared to a Maximal Marginal Relevance (MMR) baseline, the GA-based system is adept at providing relevant and diverse recommendations, particularly when generating a large set of options based on limited input. In addition, the operational speed of the genetic algorithm (GA) due to its constant-time response to varying recommendation sizes underscores its practical advantages. For customizability, the system’s linear and predictable response to adjustments in the trade-off parameters enhances its adaptability to different user needs. These advancements make the GA-based recommender system a potent tool for addressing the complexity and diversity of user preferences in large-scale applications, presenting a significant step forward in the development 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".