ORIC V2: Improved Feature Interaction Detection Model through Online Random Interaction Chains for Click-Through Rate Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it