Now showing 1 - 10 of 11
  • Publication
    Towards a reputation-based model of social web search
    (Association for Computing Machinery, 2010-02-07) ; ; ; ;
    While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.
      1459Scopus© Citations 21
  • Publication
    Social and collaborative web search : an evaluation study
    In this paper we describe the results of a live-user study to demonstrate the benefits of using the social search utility HeyStaks, a novel approach to Web search that combines ideas from personalization and social networking to provide a more collaborative search experience.
    Scopus© Citations 9  632
  • Publication
    Google shared. A case study in social search
    (Springer, 2009-07-10T09:25:48Z) ; ;
    Web search is the dominant form of information access and everyday millions of searches are handled by mainstream search engines, but users still struggle to find what they are looking for, and there is much room for improvement. In this paper we describe a novel and practical approach to Web search that combines ideas from personalization and social networking to provide a more collaborative search experience. We described how this has been delivered by complementing, rather than competing with, mainstream search engines, which offers considerable business potential in a Google-dominated search marketplace.
    Scopus© Citations 34  3433
  • Publication
    A case-based perspective on social web search
    Web search is the main way for millions of users to access information every day, but we continue to struggle when it comes to finding the right information at the right time. In this paper we build on recent work to describe and evaluate a new application of case-based Web search, one that focuses on how experience reuse can support collab- oration among searchers. Special emphasis is placed on the development of a case-based system that is compatible with existing search engines. We also describe the results of a live-user deployment.
    Scopus© Citations 17  2162
  • Publication
    Recognising and Recommending Context in Social Web Search
     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. 
    Scopus© Citations 1  404
  • Publication
    Demonstrating social search a la HeyStaks
    For all the success of mainstream search engines there are a number of opportunities for improving on the conventional Web search user experience. In this short paper we consider the default assumption that search is solitary in nature, an isolated interaction between individual user and search engine. We highlight the value of a more collaborative approach to Web search and briefy present a novel add-on for mainstream search engines: HeyStaks (www.heystaks.com). It is designed to provide a more collaborative search experience, one in which recommendation technologies play a central role, by learning from the search experiences of groups of searchers in order to provide targeted recommendations during future search sessions.
      730
  • Publication
    Collaboration and reputation in social web search
    Recent research has highlighted the inherently collaborative nature of many Web search tasks, even though collaborative searching is not supported by mainstream search engines. In this paper, we examine the activity of early adopters of HeyStaks, a collaborative Web search framework that is designed to complement mainstream search engines such as Google, Bing, and Yahoo. The utility allows users to search as normal, using their favourite search engine, while benefiting from a more collaborative and social search experience. HeyStaks supports searchers by harnessing the experiences of others, in order to enhance organic mainstream result-lists. We review some early evaluation results that speak to the practical benefits of search collaboration in the context of the recently proposed Reader-to-Leader social media analysis framework [11]. In addition, we explore the idea of utilising the reputation model introduced by McNally et al.[6] in order to identify the search leaders in HeyStaks, i.e. those users who are responsible for driving collaboration in the HeyStaks application.
      1933
  • Publication
    Recognising and recommending context in social web search
    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.
      600Scopus© Citations 1
  • Publication
    Recommending search experiences
    (Intelligent Systems Research Centre, 2011-08-31) ; ; ;
    In this paper we focus on a multi-case case-based reasoning system to support users during collaborative search tasks. In particular we describe how repositories of search experiences/knowledge can be recommended to users at search time. These recommendations are evaluated using real-world search data.
      298
  • Publication
    The altruistic searcher
    (IEEE Computer Society, 2009-08) ; ;
    Recently researchers have argued that the prevailing view of Web search, as a solitary activity, is flawed: that, in reality, Web search can be an inherently collaborative task. In this paper we describe and evaluate an approach to collaborative Web search that seeks to enhance mainstream search engines by harnessing the past search experiences of communities of likeminded searchers in order to adapt the result-lists of traditional search engines so that they reflect the niche interests of community members.
      785Scopus© Citations 4