Mixed collaborative and content-based filtering with user-contributed semantic features
Bibliographic record
Abstract
We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit item semantics. Recommender systems typically use tech-niques from collaborative filtering, in which proximity mea-sures between users are formulated to generate recommenda-tions, or content-based filtering, in which users are compared directly to items. Our approach uses similarity measures be-tween users, but also directly measures the attributes of items that make them appealing to specific users. This can be used to directly make recommendations to users, but equally im-portantly it allows these recommendations to be justified. We introduce a method for predicting the preference of a user for a movie by estimating the user’s attitude toward features with which other users have described that movie. We show that this method allows for accurate recommenda-tions for a sub-population of users, but not for the entire user population. We describe a hybrid approach in which a user-specific recommendation mechanism is learned and experi-mentally evaluated. It appears that such a recommender sys-tem can achieve significant improvements in accuracy over alternative methods, while also retaining other advantages.
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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 | 0.000 |
| 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".