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Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation

Published: 08 October 2024 Publication History

Abstract

As user behaviors become complicated on business platforms, online recommendations focus more on how to touch the core conversions, which are highly related to the interests of platforms. These core conversions are usually continuous targets, such as watch time, revenue, and so on, whose predictions can be enhanced by previous discrete conversion actions. Therefore, multi-task learning (MTL) can be adopted as the paradigm to learn these hybrid targets. However, existing works mainly emphasize investigating the sequential dependence among discrete conversion actions, which neglects the complexity of dependence between discrete conversions and the final continuous conversion. Moreover, simultaneously optimizing hybrid tasks with stronger task dependence will suffer from volatile issues where the core regression task might have a larger influence on other tasks. In this paper, we study the MTL problem with hybrid targets for the first time and propose the model named Hybrid Targets Learning Network (HTLNet) to explore task dependence and enhance optimization. Specifically, we introduce label embedding for each task to explicitly transfer the label information among these tasks, which can effectively explore logical task dependence. We also further design the gradient adjustment regime between the final regression task and other classification tasks to enhance the optimization. Extensive experiments on two offline public datasets and one real-world industrial dataset are conducted to validate the effectiveness of HTLNet. Moreover, online A/B tests on the financial recommender system also show that our model has improved significantly. Our implementation is available here1.

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  • (2024)Personalized Multi-task Training for Recommender System2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825649(413-422)Online publication date: 15-Dec-2024

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    cover image ACM Conferences
    RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
    October 2024
    1438 pages
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    Published: 08 October 2024

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    Author Tags

    1. Hybrid Targets
    2. Multi-task Learning
    3. Recommendation
    4. Task Dependence

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    • (2024)Personalized Multi-task Training for Recommender System2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825649(413-422)Online publication date: 15-Dec-2024

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