Now showing 1 - 10 of 11
  • 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.
      363
  • 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
    A Model of Collaboration-based Reputation for the Social Web
    In this paper we describe a generic approach to modeling user reputation in online social platforms based on an underlying model of collaboration. This distinguishes our approach from more conventional reputation models which are often based around ad-hoc activity metrics. We evaluate our model with respect to a conventional reputation model used by 3 social Q&A websites, each based on a different topical domain.
      41
  • Publication
    Modeling user and result reputation in collaborative web search
    (Intelligent Systems Research Centre, 2011-08-31) ; ;
    Employing collaborative recommendation techniques allows for personalization of search results in social web search. 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 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.
      204
  • Publication
    A model of collaboration-based reputation for social recommender systems
    (University College Dublin. School of Computer Science and Informatics, 2014)
    Today's online world is one full of rich interactions between its users. In the early days of the web, activity was almost exclusively solitary, now however, users regularly collaborate with one another, often mediated by a piece of content or service. In offline communities, continued good behaviour and long-term relationship building leads naturally to good reputation, however online users often remain anonymous to their community and so trust building can be difficult to foster among community members. As such there has arisen a need for the system to calculate user reputation itself. Now, online reputation systems provide a variety of benefits to the platforms that employ them such as an incentive mechanism for good behaviour and improving the robustness of the platform. However, these systems are often based on ad-hoc activity metrics, and thus do not generalise to multiple platforms or different tasks.In this thesis we introduce a novel approach to capturing and harnessing online reputation. Our approach is to develop a computational model of reputation that is based on the various types of collaboration events that naturally occur in many different types of online social platforms. We describe how a graph-based representation of these collaboration events can be used to aggregate reputation at the user-level and we evaluate a variety of different aggregation strategies. Further, we show how the availability of this type of user reputation can be used to influence traditional recommender systems by combining relevance and reputation at recommendation time. A major part of our evaluation involves integration with the HeyStaks social search system, testing our approach on real-user data from the service.
      716
  • 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.
      1342Scopus© Citations 21
  • Publication
    Models of web page reputation in social search
    To date web search has been a solitary experience for the end-user, despite the fact that recent studies highlight the potential for collaboration that is inherent in many search tasks and scenarios. As a result, researchers have begun to explore the potential for a more collaborative approach to web search, one in which the search actions of other users can influence the results returned. In this context, the expertise of other users plays an important role when it comes to ensuring the quality of recommendations that arise from their actions. The reputation of these users is important in collaborative and social search tasks, much as relevance is vital in conventional web search. In this paper we examine this concept of reputation in collaborative and social search contexts. We describe a number of different reputation models and evaluate them in the context of a particular social search service. Our results highlight the potential for reputation to improve the quality of recommendations that arise from the activities of other searchers.
      469Scopus© Citations 1
  • Publication
    Collaboration, Reputation and Recommender Systems in Social Web Search
    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.
      329Scopus© Citations 4
  • Publication
    Social and collaborative web search : an evaluation study
    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.
      565Scopus© Citations 9
  • Publication
    A comparative study of collaboration-based reputation models for social recommender systems
    Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.
      904Scopus© Citations 20