Improving the Accuracy of M-distance Based Nearest Neighbor Recommendation System by Using Ratings Variance
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Bibliographic record
Abstract
M-distance based recommendation system (MBR) is a nearest neighbor based recommendation method which uses the average of ratings given to an item as the attribute of that item. This attribute is used to determine similar items. Then, the average of the rating given to the similar items to an item of the active user determines the rating of that item. In this paper, to decrease the error of MBR, by combining the following ideas, eight MBR-based recommendation systems are proposed: (a) Using the variance of item ratings in addition to the average of item ratings, as two attributes of an item, for determining similar items in an item-based nearest neighbor method; (b) Using the variance of user ratings in addition to the average of user ratings, as two attributes of a user, for determining similar users in a user-based nearest neighbor method; (c) Using a weighted average method for combining the ratings of similar items or similar users; (d) Using ensemble learning. Experimental results on real datasets show that our proposed EVMBR and EWVMBR which use ensemble learning have the least error. The error of the suggested EWVMBR is at-least 20% lower than that of MBR, Slope-One, P-kNN, and C-kNN.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| 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 it