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Briggs, Peter
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Briggs, Peter
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Briggs, Peter
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Now showing 1 - 10 of 19
- PublicationRecommending case bases : applications in social web searchFor 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.
619 - PublicationCoping with noisy search experiencesThe 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.
1299Scopus© Citations 6 - PublicationThe altruistic searcherRecently 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.
791Scopus© Citations 4 - PublicationProvenance, trust and sharing in peer-to-peer case-based web searchDespite 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.
1433Scopus© Citations 17 - PublicationExploiting Extended Search Sessions for Recommending Search Experiences in the Social Web(Springer Berlin Heidelberg, 2012-09-03)
; ; ; ; 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.525Scopus© Citations 4 - PublicationCollaboration, Reputation and Recommender Systems in Social Web Search(Springer, 2015)
; ; ; ; 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.438Scopus© Citations 4 - PublicationRecognising and Recommending Context in Social Web SearchIn 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.
408Scopus© Citations 1 - PublicationRecommending 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.302 - PublicationHeyStaks: A Real-World deployment of Social SearchThe 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.
298Scopus© Citations 11 - PublicationSocial and collaborative web search : an evaluation study(ACM, 2011-02-16)
; ; ; ; 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.634Scopus© Citations 9