Reinforced Negative Sampling over Knowledge Graph for Recommendation
Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua
Abstract
Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples — both informative to model training and reflective of user real needs.
Topics & Concepts
Computer scienceSampling (signal processing)Artificial intelligenceMachine learningRecommender systemTraining setGraphData miningKnowledge graphMissing dataAdaptive samplingData modelingCollaborative filteringTraining (meteorology)Yield (engineering)Real world dataData collectionData samplingImportance samplingRecommender Systems and TechniquesAdvanced Graph Neural NetworksTopic Modeling