Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
Gaole He, Junyi Li, Wayne Xin Zhao, Peiju Liu, Ji-Rong Wen
Abstract
The task of Knowledge Graph Completion (KGC) aims to automatically infer the missing fact information in Knowledge Graph (KG). In this paper, we take a new perspective that aims to leverage rich user-item interaction data (user interaction data for short) for improving the KGC task. Our work is inspired by the observation that many KG entities correspond to online items in application systems. However, the two kinds of data sources have very different intrinsic characteristics, and it is likely to hurt the original performance using simple fusion strategy.
Topics & Concepts
Computer scienceLeverage (statistics)Knowledge graphAdversarial systemGraphInformation retrievalPerspective (graphical)Task (project management)Human–computer interactionMachine learningArtificial intelligenceTheoretical computer scienceManagementEconomicsAdvanced Graph Neural NetworksRecommender Systems and TechniquesTopic Modeling