Now showing 1 - 7 of 7
  • 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
    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
    Recommending topics for web curation
    A new generation of curation services provides users with a set of tools to manually curate and manage topical collections of content. However, given curation is ultimately a manual effort, it still requires significant effort on the part of the curator both in terms of collecting and managing content. We are interested in providing additional assistance to users in their curation tasks, in particular when it comes to efficiently adding content to their collection, and examine recommender systems in an effort to automate this task. We examine a number of recommendation strategies using live-user data from the popular Scoop.it curation service.
      284Scopus© Citations 4
  • 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. 
      327Scopus© Citations 1
  • 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.
      390Scopus© Citations 4
  • 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.
      537Scopus© Citations 1
  • Publication
    The Curated Web: A Recommendation Challenge
    In this paper we consider the application of content-based recommendation techniques to web curation services which allow users to curate and share topical collections of content (e.g. images, news, web pages etc.). Curation services like Pinterest are now a mainstay of the modern web and present a range of interesting recommendation challenges. In this paper we consider the task of recommending collections to users and evaluate a range of different content-based techniques across a variety of content signals. We present the results of a large-scale evaluation using data from the Scoop.it web page curation service
      99Scopus© Citations 5