Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation
裕美子 増田, Yuhao Yang, Xudong Ren, Pengfei Wang, Fangzhao Wu, Qian Wang, Chenliang Li
Abstract
Recently, exploiting a knowledge graph (KG) to enrich the semantic representation of a news article have been proven to be effective for news recommendation. These solutions focus on the representation learning for news articles with additional information in the knowledge graph, where the user representations are mainly derived based on these news representations later. However, different users would hold different interests on the same news article. In other words, directly identifying the entities relevant to the user's interest and deriving the resultant user representation could enable a better news recommendation and explanation.
Topics & Concepts
Computer scienceGraphInformation retrievalRepresentation (politics)Focus (optics)Knowledge graphPruningKnowledge representation and reasoningRecommender systemNatural language processingWorld Wide WebArtificial intelligenceTheoretical computer sciencePoliticsBiologyOpticsPolitical sciencePhysicsAgronomyLawTopic ModelingAdvanced Graph Neural NetworksRecommender Systems and Techniques