Neural Feature-aware Recommendation with Signed Hypergraph Convolutional Network
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
Understanding user preference is of key importance for an effective recommender system. For comprehensive user profiling, many efforts have been devoted to extract user feature-level preference from the review information. Despite effectiveness, existing methods mostly assume linear relationships among the users, items, and features, and the collaborative information is usually utilized in an implicit and insufficient manner, which limits the recommender capacity in modeling users’ diverse preferences. For bridging this gap, in this article, we propose to formulate user feature-level preferences by a neural signed hypergraph and carefully design the information propagation paths for diffusing collaborative filtering signals in a more effective manner. By taking the advantages of the neural model’s powerful expressiveness, the complex relationship patterns among users, items, and features are sufficiently discovered and well utilized. By infusing graph structure information into the embedding process, the collaborative information is harnessed in a more explicit and effective way. We conduct comprehensive experiments on real-world datasets to demonstrate the superiorities of our model.
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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 | 0.000 | 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 | 0.003 |
| 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