TriSeRec: A Tri-view Representation Learning Framework for Sequential/Session-based Recommendation
Bibliographic record
Abstract
Sequential/session-based recommendation models aim to learn evolving user preferences from historical user behaviors. State-of-the-art sequential/session-based recommendation models often use graph neural networks or self-attention as their building blocks. Graph neural networks excel at learning local patterns encoded in graph-structured data and have therefore shown great performance on session-based recommendation datasets, where user interactions are usually relatively short. Self-attentive models, on the other hand, are much more powerful in capturing long-range dependencies and are able to outperform graph neural network-based approaches on sequential recommendation, where longer user interactions are more frequent. As such, the recommender systems community has noted a lack of a unified framework that can simultaneously achieve great performance on both sequential and session-based recommendation. In an effort to fill this gap, this paper presents TriSeRec, a Tri-view representation learning framework for Sequential/session-based Recommendation. By converting interaction sequences into two graphical views and one sequential view, three view-specific user representations are learned by TriSeRec using graph neural networks and self-attention. The tri-view representation learning module, which is built upon the recently proposed generalized Cauchy-Schwarz divergence, disentangles and then fuses consistent and complementary information in all three views to form the final user representations for next-item predictions. Experiments on popular large-scale, real-world benchmark datasets show that TriSeRec achieves state-of-the-art performance on both sequential recommendation and session-based recommendation.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".