MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation

• URL: http://arxiv.org/abs/2202.03851v1
• Date: Tue, 8 Feb 2022 13:31:14 GMT
• Title: MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation
• Authors: Yuntao Du, Xinjun Zhu, Lu Chen, Ziquan Fang, Yunjun Gao
• Abstract summary: A knowledge graph (KG) consists of a set of interconnected typed entities and their attributes. Inspired by the success of meta-learning on scarce training samples, we propose a novel framework called MetaKG.
• Score: 20.650193619161104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A knowledge graph (KG) consists of a set of interconnected typed entities and their attributes. Recently, KGs are popularly used as the auxiliary information to enable more accurate, explainable, and diverse user preference recommendations. Specifically, existing KG-based recommendation methods target modeling high-order relations/dependencies from long connectivity user-item interactions hidden in KG. However, most of them ignore the cold-start problems (i.e., user cold-start and item cold-start) of recommendation analytics, which restricts their performance in scenarios when involving new users or new items. Inspired by the success of meta-learning on scarce training samples, we propose a novel meta-learning based framework called MetaKG, which encompasses a collaborative-aware meta learner and a knowledge-aware meta learner, to capture meta users' preference and entities' knowledge for cold-start recommendations. The collaborative-aware meta learner aims to locally aggregate user preferences for each user preference learning task. In contrast, the knowledge-aware meta learner is to globally generalize knowledge representation across different user preference learning tasks. Guided by two meta learners, MetaKG can effectively capture the high-order collaborative relations and semantic representations, which could be easily adapted to cold-start scenarios. Besides, we devise a novel adaptive task scheduler which can adaptively select the informative tasks for meta learning in order to prevent the model from being corrupted by noisy tasks. Extensive experiments on various cold-start scenarios using three real data sets demonstrate that our presented MetaKG outperforms all the existing state-of-the-art competitors in terms of effectiveness, efficiency, and scalability.

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