Deploying Different Clustering Techniques on a Collaborative-based Movie Recommender
Bibliographic record
Abstract
Recommendation systems are involved in many industries, for example (e-health, transportation, e-commerce, and agriculture), where Recommendation systems aim to benefit both market and user levels. They help consumers make the right decision based on their preferences without being exposed to data overload. Nowadays, there is a wide range of recommenders based on different filtering approaches, such as Collaborative-based, Content-based, hybrid-based, demographic-based filtering approaches. In this paper, we present clustering-based recommendation systems. We also experiment and show the results for a collaborative-based movie recommender using different clustering techniques such as Kmeans, BIRCH Balanced Iterative Reducing and Clustering using Hierarchies) and DBSCAN (Density-based Spatial Clustering of Applications with Noise). We intended to choose different clustering approaches such as partitional, hierarchical, and density-based clustering approaches. We incorporated Item-based Collaborative filtering, then applied multiple clustering techniques on the dataset based on the users' ratings. We checked the performance using accuracy measures such as MAE (Mean Absolute Error), RMSE (Root mean square error), and the computed time. These measures were calculated for analysis and comparison purposes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".