ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918129143119872 |
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| author | Luo, Huishi Zhuang, Fuzhen Zhu, Yongchun Wu, Yiqing Kang, Bo Xie, Ruobing Xia, Feng Wang, Deqing Dong, Jin |
| author_facet | Luo, Huishi Zhuang, Fuzhen Zhu, Yongchun Wu, Yiqing Kang, Bo Xie, Ruobing Xia, Feng Wang, Deqing Dong, Jin |
| contents | Dwell time (DT) is a critical post-click metric for evaluating user preference in recommender systems, complementing the traditional click-through rate (CTR). Although multi-task learning is widely adopted to jointly optimize DT and CTR, we observe that multi-task models systematically collapse their DT predictions to the shortest and longest bins, under-predicting the moderate durations. We attribute this moderate-duration bin under-representation to over-reliance on the CTR-DT spurious correlation, and propose ORCA to address it with causal-decoupling. Specifically, ORCA explicitly models and subtracts CTR's negative transfer while preserving its positive transfer. We further introduce (i) feature-level counterfactual intervention, and (ii) a task-interaction module with instance inverse-weighting, weakening CTR-mediated effect and restoring direct DT semantics. ORCA is model-agnostic and easy to deploy. Experiments show an average 10.6% lift in DT metrics without harming CTR. Code is available at https://github.com/Chrissie-Law/ORCA-Mitigating-Over-Reliance-for-Multi-Task-Dwell-Time-Prediction-with-Causal-Decoupling. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_16573 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling Luo, Huishi Zhuang, Fuzhen Zhu, Yongchun Wu, Yiqing Kang, Bo Xie, Ruobing Xia, Feng Wang, Deqing Dong, Jin Information Retrieval Dwell time (DT) is a critical post-click metric for evaluating user preference in recommender systems, complementing the traditional click-through rate (CTR). Although multi-task learning is widely adopted to jointly optimize DT and CTR, we observe that multi-task models systematically collapse their DT predictions to the shortest and longest bins, under-predicting the moderate durations. We attribute this moderate-duration bin under-representation to over-reliance on the CTR-DT spurious correlation, and propose ORCA to address it with causal-decoupling. Specifically, ORCA explicitly models and subtracts CTR's negative transfer while preserving its positive transfer. We further introduce (i) feature-level counterfactual intervention, and (ii) a task-interaction module with instance inverse-weighting, weakening CTR-mediated effect and restoring direct DT semantics. ORCA is model-agnostic and easy to deploy. Experiments show an average 10.6% lift in DT metrics without harming CTR. Code is available at https://github.com/Chrissie-Law/ORCA-Mitigating-Over-Reliance-for-Multi-Task-Dwell-Time-Prediction-with-Causal-Decoupling. |
| title | ORCA: Mitigating Over-Reliance for Multi-Task Dwell Time Prediction with Causal Decoupling |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.16573 |