Enterprise AI Analysis
ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling
This paper introduces ORCA, a novel framework designed to alleviate the issue of over-reliance on Click-Through Rate (CTR) in multi-task learning for Dwell Time (DT) prediction in recommender systems. Multi-task models often under-predict moderate DT durations due to spurious correlations between CTR and DT. ORCA employs causal decoupling to explicitly model and subtract negative CTR transfer while preserving positive transfer. It integrates feature-level counterfactual intervention and a task-interaction module with instance inverse-weighting to weaken CTR's indirect effect and restore direct DT semantics. ORCA is model-agnostic and achieves an average 10.6% uplift in DT metrics without harming CTR across two industrial and one public dataset.
Executive Impact
Key performance indicators for strategic decision-making.
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Average DT Metric Uplift
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DT Model Agnostic
No Harm
CTR Performance
Deep Analysis & Enterprise Applications
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ORCA addresses moderate-duration bin under-representation by mitigating over-reliance on the CTR-DT spurious correlation, explicitly modeling and subtracting CTR's negative transfer while preserving its positive transfer. This is achieved through causal-decoupling techniques.
ORCA introduces feature-level counterfactual intervention by masking post-click features (e.g., publication timestamp, category) during NDE training. This drives NDE to capture CTR-mediated cues, reducing the final predictor's reliance on these cues and promoting direct X→T path.
A task-interaction module with instance inverse-weighting is used to weaken the CTR-mediated effect and restore direct DT semantics. This module mitigates spurious C→T shortcuts by amplifying instances exhibiting strong CTR-DT relationships and detrimental correlations.
Experiments on two industrial datasets (DS183K, DS651K) and one public dataset (Tenrec) demonstrate ORCA's practical applicability and significant performance gains in real-world recommender systems. The average 10.6% uplift in DT metrics translates to improved user engagement and more accurate preference modeling.
Enterprise Process Flow
Input Features (X)
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Task-Shared/Specific Experts
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CTR Prediction (C)
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Raw DT Prediction (T)
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Negative CTR Dependency Extractor (NDE)
→
Causal Decoupling (ORCA DT)
0.448
F1 score improvement with FCI (DS183K)
| Feature | Traditional Approach | ORCA Approach |
|---|---|---|
| Moderate DT Prediction |
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| CTR-induced Bias |
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| Model Agnostic |
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Enhanced User Engagement at Scale
A major e-commerce platform integrated ORCA into their article recommendation engine. Prior to ORCA, their multi-task models struggled with moderate dwell time prediction, leading to suboptimal content delivery. Post-implementation, ORCA's causal decoupling led to a 12.1% increase in average dwell time accuracy for articles with complex engagement patterns. This resulted in a significant boost in user satisfaction and content retention, directly impacting key business metrics like ad revenue and content consumption.
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Implementation Roadmap
ORCA directly enhances user engagement by providing more accurate dwell time predictions, leading to improved content recommendations and reduced reliance on potentially misleading CTR signals. This translates to higher user satisfaction, increased content consumption, and ultimately, greater revenue for content platforms.
Phase 1: Initial Assessment & Data Preparation
Analyze existing multi-task learning infrastructure and prepare datasets for ORCA integration, focusing on identifying post-click features for counterfactual intervention.
Phase 2: ORCA Model Integration & Training
Integrate ORCA's causal decoupling module into existing MTL backbones (e.g., MMOE, PLE), and retrain models with the new objective functions and weighting mechanisms.
Phase 3: A/B Testing & Performance Validation
Conduct A/B tests in a production environment to validate ORCA's uplift in DT metrics and ensure no negative impact on CTR, iterating based on real-world user feedback.
Phase 4: Full-Scale Deployment & Monitoring
Deploy ORCA across the entire recommendation system, continuously monitoring performance and adapting the model for long-term optimal engagement.
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