A Language Modeling Approach to Personalized Search Based on Users’ Microblog Behavior

  • Arjumand Younus
  • Colm O’Riordan
  • Gabriella Pasi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)


Personalized Web search offers a promising solution to the task of user-tailored information-seeking, and particularly in cases where the same query may represent diverse information needs. A significant component of any Web search personalization model is the means with which to model a user’s interests and preferences to build what is termed as a user profile. This work explores the use of the Twitter microblog network as a source of user profile construction for Web search personalization. We propose a statistical language modeling approach taking into account various features of a user’s Twitter network. The richness of the Web search personalization model leads to significant performance improvements in retrieval accuracy. Furthermore, the model is extended to include a similarity measure which further improves search engine performance.


Mean Average Precision Retrieval Accuracy Twitter User Relevance Judgement Twitter Data 
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.


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  1. 1.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of twitter posts for user profile construction on the social web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)Google Scholar
  2. 2.
    Harpale, A., Yang, Y., Gopal, S., He, D., Yue, Z.: Citedata: a new multi-faceted dataset for evaluating personalized search performance. In: CIKM 2010, pp. 549–558 (2010)Google Scholar
  3. 3.
    Matthijs, N., Radlinski, F.: Personalizing web search using long term browsing history. In: WSDM 2011, pp. 25–34 (2011)Google Scholar
  4. 4.
    Noll, M.G., Meinel, C.: Web search personalization via social bookmarking and tagging. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 367–380. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Tan, B., Shen, X., Zhai, C.: Mining long-term search history to improve search accuracy. In: KDD 2006, pp. 718–723 (2006)Google Scholar
  6. 6.
    Teevan, J., Dumais, S.T., Horvitz, E.: Potential for personalization. ACM Trans. Comput.-Hum. Interact. 17(1), 4:1–4:31 (2010)Google Scholar
  7. 7.
    Vallet, D., Cantador, I., Jose, J.M.: Personalizing web search with folksonomy-based user and document profiles. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 420–431. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Wang, Q., Jin, H.: Exploring online social activities for adaptive search personalization. In: CIKM 2010, pp. 999–1008 (2010)Google Scholar
  9. 9.
    Younus, A., O’Riordan, C., Pasi, G.: Predictors of users’ willingness to personalize web search. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds.) FQAS 2013. LNCS, vol. 8132, pp. 459–470. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Arjumand Younus
    • 1
    • 2
  • Colm O’Riordan
    • 1
  • Gabriella Pasi
    • 2
  1. 1.Computational Intelligence Research Group, Information TechnologyNational University of IrelandGalwayIreland
  2. 2.Information Retrieval Lab, Informatics, Systems and CommunicationUniversity of Milan BicoccaMilanItaly

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