Cross-modal Knowledge Graph Contrastive Learning for Machine Learning Method Recommendation
Xianshuai Cao, Yuliang Shi, Jihu Wang, Han Yu, Xinjun Wang, Zhongmin Yan
Abstract
The explosive growth of machine learning (ML) methods is overloading users with choices for learning tasks. Method recommendation aims to alleviate this problem by selecting the most appropriate ML methods for given learning tasks. Recent research shows that the descriptive and structural information of the knowledge graphs (KGs) can significantly enhance the performance of ML method recommendation. However, existing studies have not fully explored the descriptive information in KGs, nor have they effectively exploited the descriptive and structural information to provide the necessary supervision. To address these limitations, we distinguish descriptive attributes from the traditional relationships in KGs with the rest as structural connections to expand the scope of KG descriptive information. Based on this insight, we propose the Cross-modal Knowledge Graph Contrastive learning (CKGC) approach, which regards information from descriptive attributes and structural connections as two modalities, learning informative node representations by maximizing the agreement between the descriptive view and the structural view. Through extensive experiments, we demonstrate that CKGC significantly outperforms the state-of-the-art baselines, achieving around 2% higher accurate click-through-rate (CTR) prediction, over 30% more accurate top-10 recommendation, and over 50% more accurate top-20 recommendation compared to the best performing existing approach.