Now showing 1 - 10 of 20
  • 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.
      652
  • Publication
    Behavioural Analysis of Mobile Web Users
    (School of Computing, Dublin City University, The Insight Centre for Data Analytics, the University of Aberdeen and Noldus, 2016-05-27) ; ; ; ; ;
    As smartphones become the predominant devices for accessing the web, understanding how individuals express their interests and interact with the web can have a great impact on several domains ranging from customer services to marketing and public policy. However, in order to better understand the web surfing behaviour and interests of mobile network subscribers, we need to look beyond the classic analytics that are based on location, internet usage and social networks. A more granular view of user behaviour and interests can be achieved by including more advanced analytics based on the content that the users are engaging with. In this paper we present a novel mobile web content analytics platform, HeyStaks, with the goal of filling the gap of granular content analysis for mobile user behavioural analytics.
      251
  • Publication
    Recommending case bases : applications in social web search
    For the main part, when it comes to questions of retrieval, the focus of CBR research has been on the retrieval of cases from a repository of experience knowledge or case base. In this paper we consider a complementary retrieval issue, namely the retrieval of case bases themselves in scenarios where experience may be distributed across multiple case repositories. We motivate this problem with reference to a deployed social web search service called HeyStaks, which is based on the availability of multiple repositories of shared search knowledge, known as staks, and which is fully integrated into mainstream search engines in order to provide a more collaborative search experience. We describe the case base retrieval problem in the context of HeyStaks, propose a number of case base retrieval strategies, and evaluate them using real-user data from recent deployments.
      526
  • 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.
      1891
  • Publication
    Trust-enhanced peer-to-peer collaborative web search
    We spend a lot of our time online using web search services, but even the leading search engines frequently fail to deliver relevant results for the vague queries that are commonplace among today’s web searchers. Interestingly, when we look at the search patterns of link-minded searchers (perhaps friends or colleagues) we do find considerable overlap between their queries and result-selections. This motivates a more collaborative approach to web search, one in which the past search experiences of friends and colleagues can be used to usefully influence our new searches. In this talk we will describe how a novel combination of casebased reasoning, web search, and peer-to-peer networking can be used to develop a platform for personalized web search, which benefits from better quality results, improved robustness against search spam, while offering an increased level of privacy to the individual user.
      363
  • Publication
    The case-based experience web
    With the rise of user-generated content (blogs, wikis, ratings, reviews, opinions etc.) the web is evolving from a repository of content into a repository of experiences, and as it evolves there are many opportunities to harness these experiences. In this paper we consider some of the challenges associated with harnessing online experiences by adopting a case-based reasoning perspective, and highlighting how existing case-based approaches might be adapted to take advantage of this new world of the experience web. To make this discussion more concrete we will draw on examples from one recent case-based attempt to harness the experiences of communities of users in the area of web search.
      618
  • 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.
      246
  • Publication
    A Case Study of Collaboration and Reputation in Social Web Search.
    Although collaborative searching is not supported by mainstream search engines, recent research has high- lighted the inherently collaborative nature of many web search tasks. In this paper, we describe HeyStaks (www.heystaks.com), a collaborative web search framework that is designed to complement mainstream search engines. At search time, HeyStaks learns from the search activities of other users and leverages this information to generate recommendations based on results that others have found relevant for similar searches. The key contribution of this paper is to extend the HeyStaks social search model by considering the search expertise, or reputation, of HeyStaks users and using this information to enhance the result recommendation process. In particular, we propose a reputation model for HeyStaks users that utilises the implicit collaboration events that take place between users as recommendations are made and selected. We describe a live-user trial of HeyStaks that demonstrates the relevance of its core recommendations and the ability of the reputation model to further improve recommendation quality. Our findings indicate that incorporating reputation into the recommendation process further improves the relevance of HeyStaks recommendations by up to 40%.
      2017Scopus© Citations 32
  • Publication
    Coping with noisy search experiences
    The so-called Social Web has helped to change the very nature of the Internet by emphasising the role of our online experiences as new forms of content and service knowledge. In this paper we describe an approach to improving main-stream Web search by harnessing the search experiences of groups of like-minded searchers. We focus on the HeyStaks system (www.heystaks.com) and look in particular at the experiential knowledge that drives its search recommendations. Specifically we describe how this knowledge can be noisy, and we describe and evaluate a recommendation technique for coping with this noise and discuss how it may be incorporated into HeyStaks as a useful feature.
      1145Scopus© Citations 6
  • Publication
    Provenance, trust and sharing in peer-to-peer case-based web search
    (Springer, 2008) ;
    Despite the success of modern Web search engines, challenges remain when it comes to providing people with access to the right information at the right time. In this paper, we describe how a novel combination of case-based reasoning, Web search, and peer-to-peer networking can be used to develop a platform for personalized Web search. This novel approach benefits from better result quality and improved robustness against search spam, while offering an increased level of privacy to the individual user.
      1294Scopus© Citations 17