Knowledge-aware Graph Attention Network with Distributed & Cross Learning for Collaborative Recommendation
Bibliographic record
Abstract
Nowadays, side-information is widely used to rein-force the user-item interaction and helps to handle the sparsity issue and cold start problem of conventional recommendation algorithms. Due to the overlook of the relationship between items and entities and the higher-order connectivity information, most existing approaches are hard to get users' deep preferences. In this paper, we propose KGANCL, a Knowledge-aware Graph Attention Network with Distributed & Cross Learning. It focuses on using different forms of the knowledge graph to strengthen both users' and items' embedding representations, respectively. Firstly, Graph Attention Network is adopted for user embedding learning, which can give different importance scores to different neighbors, and a user-item KG graph is used to integrate adjacent information to enhance the representation. Secondly, a cross module is used for item embedding learning, which shares the high-order interaction between the recommender system and the knowledge graph. We also use the idea of distributed processing for embeddings in different entities to improve the learning efficiency. Experimental results demonstrate that KGANCL can provide better recommendations compared with the state-of-the-art baseline models. Our model can also maintain superior prediction accuracy even in little-known interaction scenarios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".