Time-series association based dynamic graph evolution for recommendation
Bibliographic record
Abstract
Abstract Modeling the interactions between users and items to accurately predict ratings is very crucial for improving the performance of recommendation. Although existing graph-based methods have achieved great progress in predicting ratings for recommendation, they usually need additional side information which is difficultly obtained, and ignore the temporal associations between items (users) when constructing graphs. In this paper, we propose a time series association based dynamic graph evolution model for recommendation, which can capture not only the information propagation on multiple graphs but also the temporal association between items (users) by constructing a time series item association graph and a user similarity graph. Specifically, the proposed model consists of two main components: recurrent graph construction component and message propagation component. The former recurrently constructs the time series item association graph and the user similarity graph only from the interactions between items (users) to capture the temporal associations between items (users), which further helps the process of constructing two auxiliary graphs. The latter refines user and item representations by aggregating the influence information propagated from multiple high-order neighbors. Finally, the refined representations of users and items are used to predict ratings on all items a user has not interacted with. The experimental results illustrate that our method outperforms the state-of-the-art methods on five real-world datasets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".