Entity Burst Discriminative Model for Cumulative Citation Recommendation

Knowledge base acceleration-cumulative citation recommendation (KBA-CCR) aims to detect citation-worthiness documents from a chronological stream corpus for a set of target entities in a knowledge base. Most previous works only consider a number of semantic features between documents and target entities in the knowledge base, and then use powerful machine learning approaches such as logistic regression to classify relevant documents and non-relevant documents. However, the burst activities of an entity have been proved to be a significant signal to predict potential citations. In this paper, an entity burst discriminative model (EBDM) is presented to substantially exploit such burst features. The EBDM presents a new temporal representation based on the burst features, which can capture both temporal and semantic correlations between entities and documents. Meanwhile, in contrast to the bag-of-words model, the EBDM can significantly decrease the number of non-zero entries of feature vectors. An extensive set of experiments were conducted on the TREC-KBA-2012 dataset. The results show that the EBDM outperforms the performance of the state-of-the-art models.