A Personalized Collaborative Filtering Recommendation Algorithm Based on Linear Regression
Bibliographic record
Abstract
This paper attempts to solve the problems with linear regression-based collaborative filtering recommendation algorithm, namely, the difficulty in extracting eigenvalues, the low accuracy and the poor interpretability.For this purpose, the tag weights were introduced as eigenvalues and the prediction accuracy was improved by the principle of collaborative filtering recommendation algorithm, creating a personalized collaborative filtering recommendation algorithm based on linear regression (PCFLR).Firstly, the tag weights for users were computed by term frequency-inverse document frequency (TF-IDF), and taken as the eigenvalues of the linear regression model.Then, the linear regression model was constructed based on the users' historical scores.After that, the cost function was set up by the least squares method, and regularized to prevent over-fitting.Next, the optimal value of the cost function was computed by gradient descent method, yielding the tag weights for items.On this basis, the predicted scores of all unrated items were obtained considering the linear relationship between the tag weights for users and those for items.The mean absolute error (MAE) between the predicted and actual scores was computed, and used to adjust the predicted scores into the final results.In addition, the set of recommendable items for the target user was produced based on the scores rated by all neighboring users, and coupled with the linearly regressed scores to make recommendations to the target user.The experimental results show that the PCFLR outperformed the traditional recommendation algorithms in accuracy and interpretability.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".