http://hdl.handle.net/10379/5418 2017-10-30T00:00:49Z http://hdl.handle.net/10379/5709 Using social media for online television adaptation services at RTÉ Ireland Barraza-Urbina, Andrea; Hromic, Hugo; Heitmann, Benjamin; Tamatam, Himasagar; Yañez, Andrea; Hayes, Conor RTÉ (Raidió Teilifís Éireann) is the national provider of Television (TV) and radio in Ireland. RTÉ broadcasts its content online through the RTÉ Player and provides services to interact with its users using social media, such as Twitter and Facebook. However, RTÉ wishes to exploit the full power of knowledge that can be obtained from social media, and with that knowledge enhance their online services to further engage users. For this goal, RTÉ joined forces with The Insight Centre for Data Analytics. This document outlines the project outcomes of this collaboration. 2016-01-01T00:00:00Z http://hdl.handle.net/10379/5597 Categorising the online communities of stack exchange using quantitative user behaviour features Aumayr, Erik Maintaining online communities is vital in order to increase and retain their economic and social value. Before applying any performance altering strategies, it is important to determine the different types of communities, as they might be affected differently. In the literature, we find qualitative categories such as transactional and interest-based. However, these qualitative classification approaches do not guarantee to reflect the underlying user behaviour. Yet it is crucial to study the user behaviour, e.g. how many users join per day, in order to understand which communities perform well and which ones require intervention by a community manager. In this work, we present a bottomup community clustering approach that relies on quantitatively measurable user behaviour features. We examine 29 online communities of the Stack Exchange platform, and describe the extracted features that capture the user behaviour. Based on these features we then categorise the communities. By analysing the clusters, we find that they correspond to a certain degree to intuitive topical themes. 2016-03-02T00:00:00Z http://hdl.handle.net/10379/5557 The ACL RD-TEC: Annotation Guideline (Ver 1.0) QasemiZadeh, Behrang Annotation Guidelines for the ACL RD-TEC (ver 1.0) is set out in this document. The annotator is required to understand the meaning of term, technology term, and invalid term before commencing the annotation task. A de nition of each item is presented here. 2014-01-01T00:00:00Z http://hdl.handle.net/10379/5081 XploDiv: Diversification Approach for Recommender Systems Barraza-Urbina, Andrea; Heitmann, Benjamin; Hayes, Conor; Ramos, Angela Carrillo Recommender Systems have emerged to guide users in the task of efficiently browsing/exploring a large product space, helping users to quickly identify interesting products. However, suggestions generated with traditional Recommender Systems usually do not produce diverse results, though it has been argued that diversity is a desirable feature. The study of diversity aware Recommender Systems has become an important research challenge in recent years, drawing inspiration from diversification solutions for Information Retrieval. However, we argue it is not enough to adapt Information Retrieval techniques towards Recommender Systems, as they do not place the necessary importance to factors such as serendipity, novelty and discovery which are imperative to Recommender Systems. In this report, we propose a diversification technique for Recommender Systems that generates a diversified list of results which not only balances the trade-off between quality (in terms of accuracy) and diversity, but also considers the trade-off between exploitation of the user profile and exploration of novel products. Our experimental evaluation, composed of both qualitative and quantitative tests, shows that the proposed approach has comparable results to state of the art approaches. Moreover, through control parameters, our approach can be tuned towards more explorative or exploitative recommendations. Report 2015-01-01T00:00:00Z