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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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 2.0
Hatcher, E., Gospodnetic, O.: Lucene in action. Manning Publications (2004)
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)
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)
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)
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)
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)
Twidale, M.B., Nichols, D.M., Paice, C.D.: Browsing is a collaborative process. Information Processing & Management 33(6), 761–783 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saaya, Z., Smyth, B., Coyle, M., Briggs, P. (2011). Recognising and Recommending Context in Social Web Search. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-22362-4_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22361-7
Online ISBN: 978-3-642-22362-4
eBook Packages: Computer ScienceComputer Science (R0)