A Probabilistic Model for Time-Aware Entity Recommendation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)


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.


Dynamic Relatedness Keyword Query Related Entity Page View Anchor Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 611346.


  1. 1.
    Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the web of data. In: WWW, pp. 771–780(2010)Google Scholar
  2. 2.
    van Zwol, R., Pueyo, L.G., Muralidharan, M., Sigurbjörnsson, B.: Machine learned ranking of entity facets. In: SIGIR, pp. 879–880 (2010)Google Scholar
  3. 3.
    Kang, C., Vadrevu, S., Zhang, R., van Zwol, R., Pueyo, L.G., Torzec, N., He, J., Chang, Y.: Ranking related entities for web search queries. In: WWW Companion, vol. 67–68 (2011)Google Scholar
  4. 4.
    Blanco, R., Cambazoglu, B.B., Mika, P., Torzec, N.: Entity recommendations in web search. In: Alani, H., et al. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 33–48. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Kanhabua, N., Blanco, R., Nørvåg, K.: Temporal information retrieval. Found. Trends Inf. Retr. 9(2), 91–208 (2015)CrossRefGoogle Scholar
  6. 6.
    Nørvåg, K.: Supporting temporal text-containment queries in temporal document databases. Data Knowl. Eng. 49(1), 105–125 (2004)CrossRefGoogle Scholar
  7. 7.
    Berberich, K., Bedathur, S.J., Neumann, T., Weikum, G.: A time machine for text search. In: SIGIR, pp. 519–526 (2007)Google Scholar
  8. 8.
    Fischer, L., Blanco, R., Mika, P., Bernstein, A.: Timely semantics: a study of a stream-based ranking system for entity relationships. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 429–445. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25010-6_28 CrossRefGoogle Scholar
  9. 9.
    Yu, X., Ma, H., Hsu, B.P., Han, J.: On building entity recommender systems using user click log and freebase knowledge. In: WSDM, pp. 263–272 (2014)Google Scholar
  10. 10.
    Bi, B., Ma, H., Hsu, B.P., Chu, W., Wang, K., Cho, J.: Learning to recommend related entities to search users. In: WSDM, pp. 139–148 (2015)Google Scholar
  11. 11.
    Shen, W., Wang, J., Luo, P., Wang, M.: LINDEN: linking named entities with knowledge base via semantic knowledge. In: WWW, pp. 449–458 (2012)Google Scholar
  12. 12.
    Singh, S., Subramanya, A., Pereira, F., McCallum, A.: Wikilinks: a large-scale cross-document coreference corpus labeled via links to Wikipedia. Technical report, UM-CS-2012-015 (2012)Google Scholar
  13. 13.
    Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: AAAI Workshop on Wikipedia and Artificial Intelligence (2008)Google Scholar
  14. 14.
    Ciglan, M., Nørvåg, K.: WikiPop: personalized event detection system based on Wikipedia page view statistics. In: CIKM, pp. 1931–1932 (2010)Google Scholar
  15. 15.
    Trampuš, M., Novak, B.: Internals of an aggregated web news feed. In: SiKDD, pp. 431–434 (2012)Google Scholar
  16. 16.
    Zhang, L., Rettinger, A.: X-LiSA: Cross-lingual semantic annotation. PVLDB 7(13), 1693–1696 (2014)Google Scholar
  17. 17.
    Osborne, M., Petrovic, S., McCreadie, R., Macdonald, C., Ounis, I.: Bieber no more: first story detection using Twitter and Wikipedia. In: SIGIR 2012 Workshop on Time-aware Information Access (2012)Google Scholar
  18. 18.
    Bron, M., Balog, K., de Rijke, M.: Ranking related entities: components and analyses. In: CIKM, pp. 1079–1088 (2010)Google Scholar
  19. 19.
    Li, X., Croft, W.B.: Time-based language models. In: CIKM, pp. 469–475(2003)Google Scholar
  20. 20.
    Kanhabua, N., Nørvåg, K.: Determining time of queries for re-ranking search results. In: Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., Frommholz, I. (eds.) ECDL 2010. LNCS, vol. 6273, pp. 261–272. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Milne, D.N., Witten, I.H.: Learning to link with Wikipedia. In: CIKM, pp. 509–518 (2008)Google Scholar
  22. 22.
    Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: SIGIR, pp. 41–48 (2000)Google Scholar
  23. 23.
    Hoffart, J., Seufert, S., Nguyen, D.B., Theobald, M., Weikum, G.: KORE: keyphrase overlap relatedness for entity disambiguation. In: CIKM, pp. 545–554 (2012)Google Scholar
  24. 24.
    Coppersmith, D., Fleischer, L., Rudra, A.: Ordering by weighted number of wins gives a good ranking for weighted tournaments. ACM Trans. Algorithms 6(3), Article no. 55 (2010)Google Scholar
  25. 25.
    Balog, K., Serdyukov, P., de Vries, A.P.: Overview of the TREC 2011 entity track. In: TREC (2011)Google Scholar
  26. 26.
    Dai, N., Davison, B.D.: Freshness matters: in flowers, food, and web authority. In: SIGIR, pp. 114–121 (2010)Google Scholar
  27. 27.
    Elsas, J.L., Dumais, S.T.: Leveraging temporal dynamics of document content in relevance ranking. In: WSDM, pp. 1–10 (2010)Google Scholar
  28. 28.
    Dong, A., Zhang, R., Kolari, P., Bai, J., Diaz, F., Chang, Y., Zheng, Z., Zha, H.: Time is of the essence: improving recency ranking using Twitter data. In: WWW, pp. 331–340 (2010)Google Scholar
  29. 29.
    Kulkarni, A., Teevan, J., Svore, K.M., Dumais, S.T.: Understanding temporal query dynamics. In: WSDM, 167–176 (2011)Google Scholar
  30. 30.
    Shokouhi, M., Radinsky, K.: Time-sensitive query auto-completion. In: SIGIR, pp. 601–610 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

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

Personalised recommendations