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O'Mahony, Michael P.
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O'Mahony, Michael P.
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O'Mahony, Michael P.
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Now showing 1 - 10 of 21
- 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 - PublicationThe readability of helpful product reviewsConsumers frequently rely on user-generated product reviews to guide purchasing decisions. Given the ever increasing volume of such reviews and variations in review quality, consumers require assistance to effectively leverage this vast information source. In this paper, we examine to what extent the readability of reviews is a predictor of review helpfulness. Using a supervised classification approach, our findings indicate that readability is a useful predictor for Amazon product reviews but less so for TripAdvisor hotel reviews.
483 - 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 - PublicationTowards tagging and categorization for micro-blogs(2010-08-30)
; ; Abstract. Micro-blogging services are becoming very popular among users who want to share local or global news, their knowledge or their opinions on the real-time web. Lately, users are also using these services to search for information, and some services include tag or category information to better facilitate search. However, these tags are typically free-form in nature with users permitted to adopt their own conventions without restriction, which can make the set of tags noisy and sparse. A solution to this problem is to recommend tags (or categories) to users. Our work represents an initial study in the recommendation of categories for short-form messages in order to provide for better search and message filtering. In particular, we describe how such real-time web data can be used as a source of indexing and retrieval information for category recommendation. An evaluation performed on two different micro-blogging datasets indicates that promising performance is achieved by our approach.2200 - PublicationUsing readability tests to predict helpful product reviews(2010-04-28)
; User-generated content provides online consumers with a wealth of information. Given the ever-increasing quantity of available content and the lack of quality control applied to this content, there is a clear need to enhance the user experience when it comes to effectively leveraging this vast information source. In this paper, we address these issues in the context of user-generated product reviews. We expand on recent work to consider the performance of structural and readability feature sets on the classification of helpful product reviews. Our findings, based on a large-scale evaluation of TripAdvisor and Amazon reviews, indicate that structural and readability features are useful predictors for Amazon product reviews but less so for TripAdvisor hotel reviews.2602 - PublicationMining the Real-Time Web: A Novel Approach to Product RecommendationReal-time web (RTW) services such as Twitter allow users to express their opinions and interests, often expressed in the form of short text messages providing abbreviated and highly personalized commentary in real-time. Although this RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research, it can contain useful consumer reviews on products, services and brands. This paper describes how Twitter-like short-form messages can be leveraged as a source of indexing and retrieval information for product recommendation. In particular, we describe how users and products can be represented from the terms used in their associated reviews. An evaluation performed on four different product datasets from the Blippr service shows the potential of this type of recommendation knowledge, and the experiments show that our proposed approach outperforms a more traditional collaborative-filtering based approach.
6245Scopus© Citations 80 - PublicationLearning to recommend helpful hotel reviewsUser-generated reviews are a common and valuable source of product information, yet little attention has been paid as to how best to present them to end-users. In this paper, we describe a classification-based recommender system that is designed to recommend the most helpful reviews for a given product. We present a large-scale evaluation of our approach using TripAdvisor hotel reviews, and we show that our approach is capable of suggesting superior reviews compared to a number of alternative recommendation benchmarks.
4101Scopus© Citations 100 - PublicationA Case Study of Collaboration and Reputation in Social Web Search.(ACM, 2011-10)
; ; ; ; Although collaborative searching is not supported by mainstream search engines, recent research has high- lighted the inherently collaborative nature of many web search tasks. In this paper, we describe HeyStaks (www.heystaks.com), a collaborative web search framework that is designed to complement mainstream search engines. At search time, HeyStaks learns from the search activities of other users and leverages this information to generate recommendations based on results that others have found relevant for similar searches. The key contribution of this paper is to extend the HeyStaks social search model by considering the search expertise, or reputation, of HeyStaks users and using this information to enhance the result recommendation process. In particular, we propose a reputation model for HeyStaks users that utilises the implicit collaboration events that take place between users as recommendations are made and selected. We describe a live-user trial of HeyStaks that demonstrates the relevance of its core recommendations and the ability of the reputation model to further improve recommendation quality. Our findings indicate that incorporating reputation into the recommendation process further improves the relevance of HeyStaks recommendations by up to 40%.2184Scopus© Citations 34 - 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 - PublicationModels of web page reputation in social searchTo 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.
552Scopus© Citations 1
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