Advertisement

Recognising and Recommending Context in Social Web Search

  • Zurina Saaya
  • Barry Smyth
  • Maurice Coyle
  • Peter Briggs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)

Abstract

In this paper we focus on an approach to social search, HeyStaks that is designed to integrate with mainstream search engines such as Google, Yahoo and Bing. HeyStaks is motivated by the idea that Web search is an inherently social or collaborative activity. Heystaks users search as normal but benefit from collaboration features, allowing searchers to better organise and share their search experiences. Users can create and share repositories of search knowledge (so-called search staks) in order to benefit from the searches of friends and colleagues. As such search staks are community-based information resources. A key challenge for HeyStaks is predicting which search stak is most relevant to the users current search context and in this paper we focus on this so-called stak recommendation issue by looking at a number of different approaches to profling and recommending community-search knowledge.

Keywords

social search context recommendation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Amershi, S., Morris, M.R.: Cosearch: a system for co-located collaborative web search. In: Proceeding of the Twenty-sixth Annual SIGCHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 1647–1656. ACM, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Golovchinsky, G., Pickens, J., Back, M.: A taxonomy of collaboration in online information seeking. In: JCDL Workshop on Collaborative Information Retrieval, pp. 1–3 (2008)Google Scholar
  3. 3.
    Gruber, T.: Collective knowledge systems: Where the social web meets the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web 6(1), 4–13 (2008); Semantic Web and Web 2.0CrossRefGoogle Scholar
  4. 4.
    Hatcher, E., Gospodnetic, O.: Lucene in action. Manning Publications (2004)Google Scholar
  5. 5.
    McNally, K., O’Mahony, M.P., Smyth, B., Coyle, M., Briggs, P.: Towards a reputation-based model of social web search. In: IUI 2010: Proceeding of the 14th International Conference on Intelligent User Interfaces, pp. 179–188. ACM, New York (2010)Google Scholar
  6. 6.
    Morris, M.R., Horvitz, E.: Searchtogether: an interface for collaborative web search. In: Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, UIST 2007, pp. 3–12. ACM, New York (2007)Google Scholar
  7. 7.
    Shen, D., Pan, R., Sun, J.-T., Pan, J.J., Wu, K., Yin, J., Yang, Q.: Query enrichment for web-query classification. ACM Trans. Inf. Syst. 24, 320–352 (2006)CrossRefGoogle Scholar
  8. 8.
    Smyth, B., Briggs, P., Coyle, M., O’Mahony, M.: Google shared. a case-study in social search. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 283–294. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Smyth, B., Briggs, P., Coyle, M., O’Mahony, M.P.: A case-based perspective on social web search. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS, vol. 5650, pp. 494–508. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Twidale, M.B., Nichols, D.M., Paice, C.D.: Browsing is a collaborative process. Information Processing & Management 33(6), 761–783 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zurina Saaya
    • 1
  • Barry Smyth
    • 1
  • Maurice Coyle
    • 1
  • Peter Briggs
    • 1
  1. 1.CLARITY: Centre for Sensor Web Technologies, School of Computer Science and InformaticsUniversity College DublinIreland

Personalised recommendations