Files in This Item:
|insight_publication.pdf||5.88 MB||Adobe PDF||Download|
|Title:||Social Search||Authors:||Brusilousky, Peter
|Permanent link:||http://hdl.handle.net/10197/10400||Date:||3-May-2018||Online since:||2019-05-13T08:57:37Z||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.||Funding Details:||Science Foundation Ireland||Type of material:||Book Chapter||Publisher:||Springer||Series/Report no.:||Lecture Notes in Computer Science book series (LNCS); Volume 10100||Copyright (published version):||2018 Springer International Publishing AG||Keywords:||Recommender systems; Web search engines; Information retrieval; Social search landscape||DOI:||10.1007/978-3-319-90092-6_7||Language:||en||Status of Item:||Peer reviewed||ISBN:||978-3-319-90092-6|
|Appears in Collections:||Insight Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.