GCNE: Graph Convolution Networks with Explicitly Influence for Recommendation
Bibliographic record
Abstract
Graph Convolution Network (GCN) has become increasingly important for collaborative filtering with the modeling of user-item interaction graphs through embedding propagation.Existing work that can adapts GCN to well capture accurate user preference, which highly rely on learned representations with sufficient and high-quality training data.However, the neighborhood aggregation scheme in GCN enlarges the impact of interactions on representation learning, making the learning more vulnerable to interaction noises, since the user-item interaction graph is also modeled by same neural operations that may be unnecessary.In this paper, we propose to integrate the explicitly feedback (i.e., user-item ratings) representation of user-item interactions into the embedding process to enhance recommendation performance.We develop a novel Graph Convolution Network framework with Explicitly feedback (GCNE), which augments user-item representations by explicitly exploiting the user-item ratings feedback among entities in the predictive model, which better alleviates the interaction noises problem and data sparsity.Specifically, we introduce a adjacency matrix by regarding user behaviors and item ratings feedback as two bipartite graphs, such module could explicitly explore the propagation process of user interest and feedback influence, so as to enhance the robustness of recommendation systems.Extensive experiments demonstrate that GCNE can significantly improve the performance over various state-of-the-art baselines.Further analysis verifies the superior representation ability of our GCNE recommendation framework in alleviating the data sparsity and noise issues.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".