Cross-Task Collaborative Meta-Learning for Cold-Start Recommendations

Yantong Du, Rui Chen, Qiaoyu Tan, Qilong Han, Shenjie Wang, Xiangyu Zhao

Abstract: Optimizer-based meta-learning, specifically model-agnostic meta-learning (MAML), has emerged as a powerful tool for tackling the cold-start recommendation problem. In these meta-learning-based methods, recommendations for individual users are typically treated as separate tasks and learned independently. However, this task-by-task learning paradigm presents several observable limitations. First, learning one task at a time ignores inter-task correlations, i.e., collaborative signals, which limits the meta-model’s receptive field and prevents it from leveraging valuable shared information, ultimately leading to subpar performance. Second, the meta-model is susceptible to the task distribution, i.e., the varied preference distributions among different users, which in turn introduces biases and inconsistencies, resulting in a less robust model that may perform well on certain user groups while underperforming on others. In this paper, we explore the correlations among different tasks in cold-start recommendations and develop a novel strategy termed cross-task collaborative meta-learning (CCML). More specifically, we propose a collaborative task sampling module designed to mitigate the adverse impact of irrelevant tasks during meta-model learning. This module adaptively identifies tasks that are both similar and beneficial to the primary task, ensuring that the meta-model learns from relevant and supportive information. Additionally, to harness collaborative information across relevant tasks, we introduce a bi-level cross-task meta-training strategy. This strategy leverages multi-task learning to capture collaborative knowledge simultaneously and enhance user profiling with pertinent information. Extensive experiments on four public benchmark datasets demonstrate the advantages of CCML over many state-of-the-art cold-start recommendation methods. Our results show significant improvements in recommendation accuracy and robustness, highlighting the potential of cross-task collaboration in enhancing meta-learning-based recommender systems.

External IDs:dblp:journals/tkde/DuCTHWZ25