Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation
arxiv(2022)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要