As an effective learning paradigm against insufficient training samples, multi-task learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer , often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called task-feature collaborative learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method.

中文翻译：

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任务-特征协同学习在个性化属性预测中的应用

作为针对训练样本不足的有效学习范式，多任务学习（MTL）鼓励跨多个相关任务的知识共享，以提高整体性能。在 MTL 中，一个主要的挑战来自于将知识与不同且艰巨的任务共享的现象，称为负迁移，通常会导致性能恶化。尽管已经针对负迁移进行了大量研究，但大多数现有方法仅将迁移关系建模为任务相关性，而没有考虑跨特征和任务的迁移。与现有方法不同，我们的目标是在特征和任务之间协同减轻负迁移。为此，我们提出了一种新的多任务学习方法，称为任务特征协同学习（TFCL）。具体来说，我们首先提出了一个具有异构块对角结构正则化器的基础模型，以利用特征和任务的协作分组并抑制组间知识共享。然后我们提出了模型的优化方法。广泛的理论分析表明，我们提出的方法具有以下优点：（a）它具有全局收敛性；（b）它提供块对角结构恢复保证。作为实际的扩展，我们通过允许重叠特征和区分困难任务来扩展基本模型。我们通过用户行为的细粒度建模将其进一步应用于个性化属性预测问题。最后，模拟数据集和真实数据集的实验结果证明了我们提出的方法的有效性。我们通过用户行为的细粒度建模将其进一步应用于个性化属性预测问题。最后，模拟数据集和真实数据集的实验结果证明了我们提出的方法的有效性。我们通过用户行为的细粒度建模将其进一步应用于个性化属性预测问题。最后，模拟数据集和真实数据集的实验结果证明了我们提出的方法的有效性。