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

TL;DR

MetaKG introduces a meta-learning framework for knowledge graph-based recommendation systems, effectively addressing cold-start problems for new users and items by capturing high-order relations and semantic knowledge.

Contribution

The paper proposes a novel meta-learning approach with collaborative-aware and knowledge-aware meta learners, along with an adaptive task scheduler, to improve cold-start recommendation performance.

Findings

MetaKG outperforms state-of-the-art methods in cold-start scenarios.

The framework effectively captures high-order relations and semantic knowledge.

MetaKG demonstrates superior effectiveness, efficiency, and scalability.

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.