The cold-start problem is a long-standing challenge in recommender systems due to the lack of user-item interactions, which significantly hurts the recommendation effect over new users and items. Recently, meta-learning based methods attempt to learn globally shared prior knowledge across all users, which can be rapidly adapted to new users and items with very few interactions. Though with significant performance improvement, the globally shared parameter may lead to local optimum. Besides, they are oblivious to the inherent information and feature interactions existing in the new users and items, which are critical in cold-start scenarios. In this paper, we propose a Task aligned Meta-learning based Augmented Graph (TMAG) to address cold-start recommendation. Specifically, a fine-grained task aligned constructor is proposed to cluster similar users and divide tasks for meta-learning, enabling consistent optimization direction. Besides, an augmented graph neural network with two graph enhanced approaches is designed to alleviate data sparsity and capture the high-order user-item interactions. We validate our approach on three real-world datasets in various cold-start scenarios, showing the superiority of TMAG over state-of-the-art methods for cold-start recommendation.

翻译：冷启动问题是推荐系统中长期存在的挑战，由于缺乏用户-物品交互数据，严重影响了新用户和新物品的推荐效果。近年来，基于元学习的方法试图学习所有用户间全局共享的先验知识，该知识可快速适应仅有少量交互的新用户和新物品。尽管性能显著提升，但全局共享参数可能导致局部最优。此外，这类方法忽视了新用户和新物品中存在的固有信息与特征交互，而这些信息在冷启动场景中至关重要。本文提出一种基于任务对齐元学习的增强图（TMAG）来解决冷启动推荐问题。具体而言，我们设计了一个细粒度的任务对齐构造器，用于聚类相似用户并划分元学习任务，从而实现一致的优化方向。同时，提出一种包含两种图增强方法的增强图神经网络，以缓解数据稀疏性并捕捉高阶用户-物品交互。我们在三个真实数据集上的多种冷启动场景中验证了该方法，结果表明TMAG在冷启动推荐方面优于现有最优方法。