Attributed Graph Convolutional Network for Enhanced Social Recommendation Through Hybrid Feedback Integration
Bibliographic record
Abstract
Social recommendation, a technique aimed at predicting user preferences by harnessing social ties, has frequently employed collaborative filtering (CF) due to its demonstrated efficiency and scalability.Nonetheless, a decline in performance of most extant CF techniques has been observed when confronted with extreme sparsity in explicit feedback.Past investigations predominantly merged both explicit and implicit feedback to mitigate the data scarcity issue, embedding based solely on explicit characteristics and formulating objective functions founded on user-item associations.Such a paradigm signifies a dependency on these interactions to compensate for deficient embeddings.Notably, a considerable discrepancy exists between implicit feedback and genuine user satisfaction in social recommendations, attributed to pervasive false positive interactions devoid of detailed user/item attributes.Furthermore, the establishment of connectivity between users/items has been partially dependent on users' inclinations, suggesting that the aggregation procedure might overlook certain neighbourhood preferences.In response to these challenges, a hybrid neural graph model endowed with attributive features has been introduced.This model amalgamates explicit/implicit feedback, attribute data, and a user-item interaction graph.To counteract data sparsity, a variational graph framework has been devised to extract latent representations from both feedback and attribute data.For the effective and explicit discernment of collaborative signals, the embedding incorporates a user-item interaction graph, which offers a potent modelling of elevated-order connectivities and the detection of latent user-item associations.The user and item embeddings are derived via an attentive propagation method, with the ultimate item embeddings being sourced through a linear weighted sum, eschewing non-linear activation functions.Comparative analyses on four real-world datasets have demonstrated the superior efficacy of the proposed methodology in relation to leading contemporary recommendation systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".