Context-Aware Recommendation Systems Using Consensus-Clustering
Bibliographic record
Abstract
Recommendation Systems (RSs) have proved a compelling performance to overcome the data overload problem. Context-aware recommenders guide users/clients to more personalized recommendations. Incorporating contextual features in recommendation systems improves the systems’ accuracy; however, they still suffer from sparsity and scalability problems which impact the quality of recommendations. In this paper, to overcome these limitations, we propose a context-aware recommendation system using the notion of consensus clustering, named CARS-CC. The proposed recommendation system is experimentally evaluated using contextual Pre-filtering and Post-filtering approaches. Experimental results show that the concept of consensus learning using clustering analysis can significantly improve the recommender systems’ accuracy. The proposed method surpasses the other recommendation algorithms in terms of accuracy, precision and recall, particularly using the Hybrid Bipartite Graph Formulation (HBGF) method. In addition, CARS-CC(hgpa) has outperformed all other clustering techniques in terms of MAE and RMSE with 23.73% and 7.54%, respectively. The MAE and RMSE results show that consensus clustering leads to better accuracy measures and a more stable resilient recommendation system. The response time taken to generate recommendations using post-filtering is less than that of the pre-filtering approach. The CARS-CC(HGPA) in the post-filtering approach; generates recommendations 58.4% faster than pre-filtering, which speeds up the recommendation process and facilitates real-time response.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".