Now showing 1 - 10 of 20
  • 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.
  • 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.
  • 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.
  • 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 (, 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.
      1468Scopus© Citations 21
  • 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.
      603Scopus© Citations 1
  • 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.
      2163Scopus© Citations 17
  • 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 (, 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%.
      2189Scopus© Citations 34
  • Publication
    Exploiting Extended Search Sessions for Recommending Search Experiences in the Social Web
    HeyStaks is a case-based social search system that allows users to create and share case bases of search experiences (called staks) and uses these staks as the basis for result recommendations at search time. These recommendations are added to conventional results from Google and Bing so that searchers can benefit from more focused results from people they trust on topics that matter to them. An important point of friction in HeyStaks is the need for searchers to select their search context (that is, their active stak) at search time. In this paper we extend previous work that attempts to eliminate this friction by automatically recommending an active stak based on the searchers context (query terms, Google results, etc.) and demonstrate significant improvements in stak recommendation accuracy.
      527Scopus© Citations 4
  • Publication
    HeyStaks: A Real-World deployment of Social Search
    The purpose of this paper is to provide a deployment update for the HeyStaks social search system which uses recommendation techniques to add collaboration to mainstream search engines such as Google, Bing, and Yahoo. We describe our the results of initial deployments, including an assessment of the quality of HeyStaks' recommendations, and highlight some lessons learned in the marketplace.
      301Scopus© Citations 11
  • 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.
      1434Scopus© Citations 17