ORIC V2: Improved Feature Interaction Detection Model through Online Random Interaction Chains for Click-Through Rate Prediction
Bibliographic record
Abstract
Predicting the probability that a user clicks a specific item is fundamental in online advertising and recommendation. Further, it is crucial to use the latest and historical data appropriately in online scenarios to train CTR models. Online Random Interaction Chains (ORIC) was proposed to detect informative and interpretable feature interactions without retraining on historical data in online scenario, and the Streaming Integrated Model (SIM) framework was designed to integrate these time-varying feature interactions into CTR prediction models. Unfortunately, ORIC exhibits latency when provides the feature interactions used to evaluate SIM, and ORIC is not applicable for numerical features. For these reasons, we propose ORIC-V2 that uses time series models to predict the confidence of candidate evaluating feature interactions and selects reasonable feature interactions, and combines numerical features with ORIC-V2 through a discretization model to obtain DORIC-V2. Feeding the feature interactions found by ORIC-V2 and DORIC-V2 into SIM obtains significant experimental results on three datasets, demonstrating the effectiveness and interpretability of ORIC-V2 and DORIC-V2.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".