Beyond Time: Dynamic Context-Aware Entity Recommendation

  • Nam Khanh TranEmail author
  • Tuan Tran
  • Claudia Niederée
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10249)


Entities and their relatedness are useful information in various tasks such as entity disambiguation, entity recommendation or search. In many cases, entity relatedness is highly affected by dynamic contexts, which can be reflected in the outcome of different applications. However, the role of context is largely unexplored in existing entity relatedness measures. In this paper, we introduce the notion of contextual entity relatedness, and show its usefulness in the new yet important problem of context-aware entity recommendation. We propose a novel method of computing the contextual relatedness with integrated time and topic models. By exploiting an entity graph and enriching it with an entity embedding method, we show that our proposed relatedness can effectively recommend entities, taking contexts into account. We conduct large-scale experiments on a real-world data set, and the results show considerable improvements of our solution over the states of the art.


Contextual entity relatedness Entity recommendation 



This work was partially funded by the German Federal Ministry of Education and Research (BMBF) for the project eLabour (01UG1512C).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.L3S Research CenterLeibniz Universität HannoverHanoverGermany

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