GCNE: Graph Convolution Networks with Explicitly Influence for Recommendation
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
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.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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