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Social Search

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Social Information Access

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10100))

Abstract

Today, most people find what they are looking for online by using search engines such as Google, Bing, or Baidu. Modern web search engines have evolved from their roots in information retrieval to developing new ways to cope with the unique nature of web search. In this chapter, we review recent research that aims to make search a more social activity by combining readily available social signals with various strategies for using these signals to influence or adapt more conventional search results. The chapter begins by framing the social search landscape in terms of the sources of data available and the ways in which this can be leveraged before, during, and after search. This includes a number of detailed case studies that serve to mark important milestones in the evolution of social search research and practice.

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Notes

  1. 1.

    http://amazon.com.

  2. 2.

    http://yelp.com.

  3. 3.

    https://del.icio.us.

  4. 4.

    https://flickr.com.

  5. 5.

    http://www.citeulike.org.

  6. 6.

    http://www.semanticscholar.org/.

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Authors and Affiliations

  1. School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Peter Brusilovsky

  2. Insight Centre for Data Analytics School of Computer Science, University College Dublin, Dublin, Ireland

    Barry Smyth

  3. Department of software and Information Systems Engineering, Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel

    Bracha Shapira

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  1. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Peter Brusilovsky

  2. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Daqing He

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Brusilovsky, P., Smyth, B., Shapira, B. (2018). Social Search. In: Brusilovsky, P., He, D. (eds) Social Information Access. Lecture Notes in Computer Science(), vol 10100. Springer, Cham. https://doi.org/10.1007/978-3-319-90092-6_7

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