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 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".