A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation

• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2018,（6）
• 页码范围: 660-670
• 页数: 11页
• 语种: 英文
• DOI: 10.26599/TST.2018.9010011

摘要

Knowledge Bases (KBs) are valuable resources of human knowledge which contribute to many applications.However,since they are manually maintained,there is a big lag between their contents and the up-to-date information of entities.Considering a target entity in KBs,this paper investigates how Cumulative Citation Recommendation (CCR) can be used to effectively detect its worthy-citation documents in large volumes of stream data.Most global relevant models only consider semantic and temporal features of entity-document instances,which does not sufficiently exploit prior knowledge underlying entity-document instances.To tackle this problem,we present a Mixture of Experts (ME) model by introducing a latent layer to capture relationships between the entity-document instances and their latent class information.An extensive set of experiments was conducted on TREC-KBA-2013 dataset.The results show that the model can significantly achieve a better performance gain compared to state-of-the-art models in CCR.