Now showing 1 - 10 of 22
  • Publication
    Aggregating Content and Network Information to Curate Twitter User Lists
    Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the "crowdsourcing" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these different "views" of a news story on Twitter to produce more accurate user recommendations to support the curation process.
    Scopus© Citations 12  591
  • Publication
    Remote asynchronous collaborative web search : a community-based approach
    Recently researchers have argued that the prevailing view of web search, as a solitary activity, is flawed: that, in reality, web search is often 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 like-minded searchers in order to adapt the result-lists of traditional search engines so that they reflect the niche interests of community members.
      528
  • 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
  • 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
    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
    Introducing social networks and brain computer interaction
    (National University of Ireland, Galway, 2012-06-21) ; ; ;
    It is well known that the brain generates electrical patterns of activity in response to visual stimuli such as faces or any- thing that captures attention in a significant way. Signals of this type can be detected using an EEG (Electroencephalograph) system where we attach electrodes to the scalp and we amplify the detected signals and use a computer to capture them in real time. In this paper we examine the role that automatic sensing of brain activity may have on how users interact with interactive applications like Facebook. This offers a new opportunity for implicit feedback into such systems and in our work we focus on social networking applications. We demonstrate some of these implicit responses with experimental data captured while a user searched Facebook for photos of friends while being connected to an EEG. Finally, we discuss the implications that this kind of automatic implicit feedback may have on future design of such systems.
      3312
  • Publication
    Evaluating user reputation in collaborative web search
    Often today’s recommender systems look to past user activity in order to influence future recommendations. In the case of social web search, employing collaborative recommendation techniques allows for personalization of search results. If recommendations arise from past user activity, the expertise of those users driving the recommendation process can play an important role when it comes to ensuring recommendation quality. Hence the reputation of users is important in collaborative and social search tasks, in addition to result relevance as traditionally considered in web search. In this paper we explore this concept of reputation; specifically, investigating how reputation can enhance the recommendation engine at the core of the HeyStaks social search utility. We evaluate a number of different reputation models in the context of the HeyStaks system, and demonstrate how incorporating reputation into the recommendation process can enhance the relevance of results recommended by HeyStaks.
      497
  • Publication
    Visual interfaces for improved mobile search
    (Sun SITE Central Europe (CEUR), 2009) ; ;
    The Mobile Web promises a new age of anytime, anywhere information access to billions of users across the globe. However, the Mobile Internet represents a challenging information access environment, particularly from a search standpoint. In this paper we present two visual interfaces for improved mobile search. First, we present SearchBrowser, a map-based interface that offers richer end-user interactions by taking into account important mobile contexts including location and time. Second, we consider the social context of mobile search and present Social Search Browser; a proofof-concept interface that incorporates social networking capabilities to improve the search and information discovery experience of mobile subscribers.
      1304
  • 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