Retrieval Augmented Cross-Domain LifeLong Behavior Modeling for Enhancing Click-through Rate Prediction
Bibliographic record
Abstract
Lifelong behavior modeling for single-domain has been widely investigated in industry click-through (CTR) prediction. However, some domains do not always have rich historical behaviors in online platforms, so cross-domain lifelong behavior modeling is overlooked. This paper proposes a novel retrieval augmented lifelong cross-domain net (RAL-CDNet) to address the challenges in cross-domain lifelong behavior modeling. There are three components in RAL-CDNet, i.e., cross-domain retrieval unit, cross-domain alignment unit, and cross-net. As the general search unit in the previous study, a cross-domain retrieval unit features a retrieval augmented paradigm that utilizes a pre-trained language model to learn the intrinsic textual information of user behaviors and generates the sequential behaviors from the source domain based on sequential behaviors in the target domain. The retrieval augmented behaviors can achieve consistency and capture accurate hidden interest for target domain CTR prediction. Furthermore, we propose the cross-domain alignment unit to align the embeddings across domains by adding a semantic-guided contrastive loss and auxiliary task loss in the source domain. This allows the embeddings to be consistent across domains and have enough source information to capture the cross-domain relation. Finally, the cross-net utilizes two-level attention techniques to enhance the final prediction in the target domain. We conduct extensive experiments on both a public dataset and an industrial dataset from the WeChat advertising platform to demonstrate the effectiveness of RAL-CDNet in terms of offline and online metrics.
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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 | 0.002 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".