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Smyth, Barry
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Smyth, Barry
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Smyth, Barry
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- PublicationCollaboration and reputation in social web search(2010-09)
; ; ; ; 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.1938 - PublicationContent on demand for fourth year advanced materials and manufacturing students(International Symposium of Engineering Education, 2012-07-18)
; ; ; ; ; There is growing recognition of the key role that social and informal learning play in Higher Education. There is also increasing interest in technologies that enable, capture and channel this type of learning to students at their point of need and personalised to their ability. The objective of this project was to leverage research technologies from the areas of adaptive hypermedia, social and semantic search to create an application to deliver learning resources to students tailored to their specific learning needs. In this project, some 130 digital learning resources, specific to a final year advanced materials and manufacturing module, were made available to the students via a Help Block plugin in the Moodle Virtual Learning Environment. The students were required to use the Help Block as a just-in-time learning resource to help them complete a continuous assessment assignment. The assignment required the students to select an advanced manufacturing process and associated material describing the manufacturing process steps, control and specifications and presenting the technological benefits of the process and material used relative to competing processes and materials. Post-trial, students were asked to complete a questionnaire to describe their experience with the Help Block in terms of whether it assisted them in completing the assignment, for example, and its ease of use. The system, evaluation findings, and some suggestions for future system enhancements are presented in the paper.180 - PublicationSupporting Problem-based Learning in Moodle using Personalised, Context- specific Learning Episode Generation(Moodle Research Conference, 2012-09-14)
; ; ; ; ; Providing learners with a list of disparate search results is not always conducive to learning. In particular, this approach lacks learning structure, and learners have to sift through lists of resources in order to make sense of them and to find the level of detail they require. In this paper we outline the Moodle Help Block, a Moodle block plug-in that provides learners with Just-In-Time context relevant learning material using a defined pedagogical strategy. The Moodle Help Block uses a combination of Semantic Web, Social Web and learning composition technology to generate learning episodes as needed by learners. The Moodle help block conducts a dialogue with the learner to extrapolate where a given learner’s knowledge gaps lie and generate learning episodes with learning material to help the learner overcome their knowledge deficit. It is thought that the Moodle Help Block can assist learners with targeted help when a teacher is not available.173 - PublicationFurther experiments in micro-blog categorization(Intelligent Systems Research Centre, 2011-08-31)
; ; Since the creation of Twitter in 2008, micro-blogging services have received a lot of attention among users who wish to share news items, opinions and information with friends and colleagues. However, these services typically provide for only limited organisation of content, with the main ranking criterion being post time with perhaps some basic message filtering accommodated. Given the substantial and increasing volume of posts that micro-blogging services attract, there is a clear need to assist users when it comes to effectively consuming this content. In this regard, categorisation offers one approach to organise content by grouping related messages together. In this paper we present a study in the recommendation of categories for short-form messages in order to provide for better search and message filtering. In particular, we present an index-based approach where real-time web data can be used as a source of knowledge for category recommendation. Further, we evaluate our approach on two different micro-blogging datasets and results show that micro-blog messages in sufficient quantities provide a useful recommendation signal for category recommendation.1031 - PublicationA multi-criteria evaluation of a user generated content based recommender system(2011-10-23)
; ; The Social Web provides new and exciting sources of information that may be used by recommender systems as a complementary source of recommendation knowledge. For example, User-Generated Content, such as reviews, tags, comments, tweets etc. can provide a useful source of item information and user preference data, if a clear signal can be extracted from the inevitable noise that exists within these sources. In previous work we explored this idea, mining term-based recommendation knowledge from user reviews, to develop a recommender that compares favourably to conventional collaborative-filtering style techniques across a range of product types. However, this previous work focused solely on recommendation accuracy and it is now well accepted in the literature that accuracy alone tells just part of the recommendation story. For example, for many, the promise of recommender systems lies in their ability to surprise with novel recommendations for less popular items that users might otherwise miss. This makes for a riskier recommendation prospect, of course, but it could greatly enhance the practical value of recommender systems to end-users. In this paper we analyse our User-Generated Content (UGC) approach to recommendation using metrics such as novelty, diversity, and coverage and demonstrate superior performance, when compared to conventional user-based and item- based collaborative filtering techniques, while highlighting a number of interesting performance trade-offs.1015 - PublicationTopic Extraction from Online Reviews for Classification and RecommendationAutomatically identifying informative reviews is increasingly important given the rapid growth of user generated reviews on sites like Amazon and TripAdvisor. In this paper, we describe and evaluate techniques for identifying and recommending helpful product reviews using a combination of review features, including topical and sentiment information, mined from a review corpus.
872 - PublicationA recommender system approach to enhance web search and query formulation(2008)
; ; ; While search engines are the primary means by which information is located online, significant issues remain when trying to satisfy the needs of searchers, especially in the face of the type of vague queries that dominate Web search. In this paper, we tackle this problem by applying a recommender system approach to Web search which allows users to dynamically interact with the result-space that is of interest to them. Our proposed recommendation interface also facilitates query expansion through a context-sensitive tag cloud, helping searchers to efficiently assimilate potential expansion terms that are mined from results of interest. We present findings from a live user trial of our approach which indicate, for example, that it facilitates users to locate relevant information more quickly when compared to using standard search engine result lists.908 - PublicationPredicting helpful product reviews(2010-08-30)
; ; Millions of users are today posting user-generated content online, expressing their opinions on all manner of goods and services, topics and social affairs. While undoubtedly useful,user-generated content presents consumers with significant challenges in terms of information overload and quality considerations. In this paper, we address these issues in the context of product reviews and present a brief survey of our work to date on predicting review helpfulness. In particular, the performance of a variety of different machine learning approaches is evaluated on four large-scale review datasets drawn from the TripAdvisor and Amazon domains. Our findings highlight some interesting properties of this task from a machine learning perspective and demonstrate that author reputation, the sentiment expressed in reviews and review length are among the most effective predictors of review helpfulness.768 - PublicationThe Reviewer's Assistant: Recommending Topics to Writers by Association Rule Mining and Case-base Reasoning(2012-09-17)
; ; ; Today, online reviews for products and services have become an important class of user-generated content and they play a valuable role for countless online businesses by helping to convert casual browsers into informed and satisfied buyers. As users gravitate towards sites that offer insightful and objective reviews, the ability to source helpful reviews from a community of users is increasingly important. In this extended abstract we describe the Reviewer’s Assistant, a case-based reasoning inspired recommender system designed to help people to write more helpful reviews on sites such as Amazon and TripAdvisor. In particular, we describe two approaches to helping users during the review writing process and evaluate each as part of a blind live-user study. Our results point to high levels of user satisfaction and improved review quality compared to a control-set of Amazon reviews.199 - 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.632Scopus© Citations 9