Combining Collaborative Filtering and Clustering for Implicit Recommender System
Bibliographic record
Abstract
Recommender systems are becoming a widespread technology used to promote cross-selling. Collaborative filtering is one of the main paradigms employed to offer recommendations to users. However, while most collaborative filtering methods require explicit user feedback, such as ratings, it is a well-established fact that users rate only a small portion of all available products. Subsequently, the rating system often acquires insufficient explicit feedback, thus leading to unsatisfactory recommendations. We propose a novel approach in the implicit feedback recommender system domain that combines clustering and matrix factorization to yield good results while using only implicit feedback on users purchase history and without requiring any parameter. We use a high-dimensional, parameter-free, divisive hierarchical clustering technique and, based on the clustering results, create personalized recommendations of high interest for each user. This easy to implement and very effective technique can be applied to any data sets where we can identify users with a purchase history.
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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 | 0.001 |
| 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".