A Probabilistic Model for Time-Aware Entity Recommendation

Conference paper

DOI: 10.1007/978-3-319-46523-4_36

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)
Cite this paper as:
Zhang L., Rettinger A., Zhang J. (2016) A Probabilistic Model for Time-Aware Entity Recommendation. In: Groth P. et al. (eds) The Semantic Web – ISWC 2016. ISWC 2016. Lecture Notes in Computer Science, vol 9981. Springer, Cham


In recent years, there has been an increasing effort to develop techniques for related entity recommendation, where the task is to retrieve a ranked list of related entities given a keyword query. Another trend in the area of information retrieval (IR) is to take temporal aspects of a given query into account when assessing the relevance of documents. However, while this has become an established functionality in document search engines, the significance of time has not yet been recognized for entity recommendation. In this paper, we address this gap by introducing the task of time-aware entity recommendation. We propose the first probabilistic model that takes time-awareness into consideration for entity recommendation by leveraging heterogeneous knowledge of entities extracted from different data sources publicly available on the Web. We extensively evaluate the proposed approach and our experimental results show considerable improvements compared to time-agnostic entity recommendation approaches.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute AIFBKarlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations