SessNet: A Deep Hybrid-state Session-based Recommender System
Bibliographic record
Abstract
The present article studies static and dynamic signals in Session-based recommender systems in order to track and estimate users' actions. Session-based recommendation is the task of predicting user actions during short online sessions. Previous work considers the user to be anonymous in this setting, with no past behavior history available. In reality, this is not often the case. A seamless integration of the user history when available has not been offered prior to the present work in the session-based recommendation context. In this paper, we propose a novel deep hybrid-state session-based recommender system, called SessNet. SessNet performs next-click prediction and takes advantage of historical user preferences when accessible. To that end, SessNet's architecture is designed to be a hybrid of two states, namely, the dynamic and static states. First, the dynamic state, adopted from CyberBERT, employs a bidirectional transformer network to model short-term and long-term session intent. Second, the novel static state provides a deep user profile, drawing on rich item and session embeddings obtained from the dynamic state. These user representations along with the current session are processed to predict the next click. We evaluate the efficacy of the proposed method using a benchmark dataset, namely, DIGINETICA. Experiments show that our architecture achieves state-of-the-art session-based recommendation for P@20 and MRR @ 20 on this dataset.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".