Collaboration, Reputation and Recommender Systems in Social Web Search
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|Title:||Collaboration, Reputation and Recommender Systems in Social Web Search||Authors:||Smyth, Barry; Coyle, Maurice; Briggs, Peter; McNally, Kevin; O'Mahony, Michael P.||Permanent link:||http://hdl.handle.net/10197/10874||Date:||2015||Online since:||2019-07-10T11:01:13Z||Abstract:||Modern web search engines have come to dominate how millions of people find the information that they are looking for online. While the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last two decades, mainstream search is not without its challenges. Mainstream search engines continue to provide a largely one-size-fits-all service to their user-base, ultimately limiting the relevance of their result-lists. And they have only very recently begun to consider how the rise of the social web may support novel approaches to search and discovery, or how such signals can be used to inform relevance. In this chapter we will explore recent research which aims to do just that: to make web search a more personal and collaborative experience and to leverage important information such as the reputation of searchers during result-ranking. In short we look towards a more social future for mainstream search.||Funding Details:||Science Foundation Ireland||Type of material:||Book Chapter||Publisher:||Springer||Start page:||569||End page:||608||Copyright (published version):||2015 Springer||Keywords:||Search engine; Recommender system; User profile; Relevance feedback; Reputation system||DOI:||10.1007/978-1-4899-7637-6_17||Language:||en||Status of Item:||Peer reviewed||Is part of:||Ricci, F., Rokach, L., Shapira, B. (eds.). Recommender Systems Handbook||ISBN:||978-1-4899-7637-6|
|Appears in Collections:||Insight Research Collection|
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