Advertisement

Context of Seasonality in Web Search

  • Tomáš Kramár
  • Mária Bieliková
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

Abstract

In this paper we discuss human behavior in interaction with information available on the Web via search. We consider seasonality as a novel source of context for Web search and discuss the possible impact it could have on search results quality. Seasonality is used in recommender systems as an attribute of the recommended item that might influence its perceived usefulness for particular user. We extend this idea to Web search, introduce a seasonality search context, describe the challenges it brings to Web search and discuss its applicability. We present our analysis of AOL log that shows that the level of seasonal behavior varies.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beitzel, S.M., Jensen, E.C., Chowdhury, A., Grossman, D., Frieder, O.: Hourly analysis of a very large topically categorized web query log. In: Proc. of the 27th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, SIGIR 2004, pp. 321–328. ACM, New York (2004)CrossRefGoogle Scholar
  2. 2.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2), 224–227 (1979)CrossRefGoogle Scholar
  3. 3.
    Downey, D., Dumais, S., Liebling, D., Horvitz, E.: Understanding the relationship between searchers’ queries and information goals. In: Proc. of the 17th ACM Conf. on Information and Knowledge Management, CIKM 2008, pp. 449–458. ACM (2008)Google Scholar
  4. 4.
    Kramár, T., Barla, M., Bieliková, M.: Disambiguating search by leveraging a social context based on the stream of user’s activity. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 387–392. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Kříž, J.: Keyword extraction based on implicit feedback. Bulletin of ACM Slovakia 4(2), 43–46 (2012)Google Scholar
  6. 6.
    Lawrence, S.: Context in web search. IEEE Data Eng. Bulletin 23(3), 25–32 (2000)Google Scholar
  7. 7.
    Marinho, L.B.: et al.: Improving location recommendations with temporal pattern extraction. In: Proc. of the 18th Brazilian Symposium on Multimedia and the Web, WebMedia 2012, pp. 293–296. ACM (2012)Google Scholar
  8. 8.
    Metzler, D., Jones, R., Peng, F., Zhang, R.: Improving search relevance for implicitly temporal queries. In: Proc. of the 32nd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, SIGIR 2009, pp. 700–701. ACM, New York (2009)Google Scholar
  9. 9.
    Shokouhi, M.: Detecting seasonal queries by time-series analysis. In: Proc. of the 34th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, SIGIR 2011, pp. 1171–1172. ACM, New York (2011)Google Scholar
  10. 10.
    Smyth, B.: Social and personal: communities and collaboration in adaptive web search. In: Proc. of the 1st Int. Conf. on Information Interaction in Context, IIiX, pp. 3–5. ACM (2006)Google Scholar
  11. 11.
    Smyth, B., Coyle, M., Briggs, P.: Heystaks: a real-world deployment of social search. In: Proc. of the Sixth ACM Conf. on Recommender Systems, RecSys 2012, pp. 289–292. ACM (2012)Google Scholar
  12. 12.
    White, R.W., Bennett, P.N., Dumais, S.T.: Predicting short-term interests using activity-based search context. In: Proc. of the 19th ACM Int. Conf. on Information and Knowledge Management, CIKM 2010, pp. 1009–1018. ACM (2010)Google Scholar
  13. 13.
    Zhang, Y., Jansen, B.J., Spink, A.: Time series analysis of a web search engine transaction log. Information Processing and Management 45(2), 230–245 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tomáš Kramár
    • 1
  • Mária Bieliková
    • 1
  1. 1.Faculty of Informatics and Information TechnologiesSlovak University of TechnologyBratislavaSlovakia

Personalised recommendations