UDIS: Enhancing Collaborative Filtering with Fusion of Dimensionality Reduction and Semantic Similarity
Bibliographic record
Abstract
In the era of vast information, individuals are immersed in choices when purchasing goods and services. Recommender systems (RS) have emerged as vital tools to navigate these excess options. However, these systems encounter challenges like data sparsity, impairing their effectiveness. This paper proposes a novel approach to address this issue and enhance RS performance. By integrating user demographic data, singular value decomposition (SVD) clustering, and semantic similarity in collaborative filtering (CF), we introduce the UDIS method. This method amalgamates four prediction types—user-based CF (U), demographic-similarity-based (D), item-based CF (I), and semantic-similarity-based (S). UDIS generates separate predictions for each category and evaluates four different merging techniques—the average, max, weighted sum, and Shambour methods—to integrate these predictions. Among these, the average method proved most effective, offering a balanced approach that significantly improved precision and accuracy on the MovieLens dataset compared to alternative methods.
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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 | 0.000 | 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 | 0.000 |
| 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".