Similarity Measure Based on Low-Rank Approximation for Highly Scalable Recommender Systems
Bibliographic record
Abstract
Recommender systems are mostly used to make the appropriate personalized recommendation for different customers. Collaborative filtering recommendation is one of the most popular methods among E-commerce systems, but it has some shortcomings, such as cold starts, in which the system fails to consider items which no one in the community has rated previously, and sparse data, which is caused by a low number of rankings by users which results in a sparse similarity matrix. Most of the existing approaches have shortcomings of sparsity and scalability. In this paper we propose a method that approximates the matrix of users similarities with Nystrom lowrank approximations and is based on Collaborative Filtering (CF). The proposed method avoids the high computation cost of Singular Value Decomposition (SVD) and also enables us to use the low-rank approximation of the similarity matrix to handle huge datasets with low computation costs. The experimental results show that the proposed approach can solve the problem of sparsity, while increasing the efficiency and scalability of the system.
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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 | 0.002 | 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 | 0.001 | 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".