MSRHNN:Multidimensional Social Relation under Heterogeneous Neural Network for Recommendation
Bibliographic record
Abstract
Abstract With the growing popularity of mobile smart devices and the availability of 4G and 5G networks, social recommendation systems have become a hot research topic for industrial applications. Social networks among users are built through social information, which can improve collaborative filtering and solve cold start and sparsity problems. Graph convolutional networks have been widely used to represent the interactions and structural relationships among entities, but the exponential increase of the domain introduces a new problem. In this paper, we propose a multidimensional social relation under heterogeneous neural network (MSRHNN). By embedding historical evaluations, various social networks constituting different dimensions, the attention integration of different social networks on user preferences is achieved. A static graph encoder is used to process the node representation of each heterogeneous graph at different time steps, which includes the node attribute information and edge information, to capture the changing feature information of the nodes in all time steps of the dynamic graph, and to obtain an effective node representation at each time step. The goal of the heterogeneous dynamic graph neural network is to capture the changing node representations of the same nodes and different nodes in the graph network at different time steps, in order to better perform the task of node classification in dynamic graphs. In this paper, the model of heterogeneous dynamic graph neural network is verified from data, and experiments show that heterogeneous dynamic graph neural network outperforms other representation learning methods.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".