@article{Ma2018, 
author = {Lerong Ma and Lejian Liao and Dandan Song and Jingang Wang},
title = {A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation},
year = {2018},
journal = {Tsinghua Science and Technology},
volume = {23},
number = {6},
pages = {660-670},
keywords = {knowledge base acceleration, cumulative citation recommendation, Mixture of Experts (ME), Latent Entity-Document Classes (LEDCs)},
url = {https://www.sciopen.com/article/10.26599/TST.2018.9010011},
doi = {10.26599/TST.2018.9010011},
abstract = {Knowledge Bases (KBs) are valuable resources of human knowledge which contribute to manyapplications. 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.}
}