Adaptive collaborative filtering based on user-genre-item relation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Collaborative filtering provides personalised recommendations based on individual user preferences as well as those of other users with similar interests. In collaborative filtering, memory-based approaches make predictions by measuring the whole similarity between two users. When a user has multiple interest genres, those methods seem too optimistic in making correct predictions in some situations. In addition, minor genres are often inhibited due to their minute share of the whole similarity. In this paper, we present a novel approach that combines the advantages of item-item similarity and user-user similarity by introducing a genre component to the relation between user and item. In our approach, the direct user-item relevance is developed into a combination of genre similarity and preference similarity, thus capturing more accurately the relevance between items as well as between user and item. Experimental results from EachMovie and MovieLens datasets show that our approach outperforms four other state-of-the-art collaborative filtering algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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