Browsing by Author "McClean, S.I."

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    Collaborative Filtering: The Aim of Recommender Systems and the Significance of User Ratings
    (Springer, 2010-03) Redpath, J.; Glass, D.; Chen, Liming; McClean, S.I.
    This paper investigates the significance of numeric user ratings in recommender systems by considering their inclusion / exclusion in both the generation and evaluation of recommendations. When standard evaluation metrics are used, experimental results show that inclusion of numeric rating values in the recommendation process does not enhance the results. However, evaluating the accuracy of a recommender algorithm requires identifying the aim of the system. Evaluation metrics such as precision and recall evaluate how well a system performs at recommending items that have been previously rated by the user. By contrast, a new metric, known as Approval Rate, is intended to evaluate how well a system performs at recommending items that would be rated highly by the user. Experimental results demonstrate that these two aims are not synonymous and that for an algorithm to attempt both obscures the investigation. The results also show that appropriate use of numeric rating valuesin the process of calculating user similarity can enhance the performance when Approval Rate is used.
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    Managing Sensor Data in Ambient Assisted Living
    (The Korean Institute of Information Scientists and Engineers, 2011-11) Nugent, Chris; Galway, L.; McClean, S.I.; Zhang, S.; Scotney, L.; Chen, Liming; Donnelly, Mark P.; Parr, G.
    The use of technology within the home has gained wide spread acceptance as one possible approach to be used in addressing the challenges of an ageing society. A number of rudimentary assistive solutions are now being deployed in real settings but with the introduction of these technology-orientated services come a number of challenges, which to date are still largely unsolved. At a fundamental level, the management and processing of the large quantities of data generated from multiple sensors is recognised as one of the most significant challenges. This paper aims to present an overview of the types of sensor technologies used within Ambient Assisted Living. Subsequently, through presentation of a series of case studies, the paper will demonstrate how the practical integration of multiple sources of sensor data can be used to improve the overall concept and applications of Ambient Assisted Living
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    User-based Collaborative Filtering: Sparsity and Performance
    (IOS Press, 2010-12-14) Chen, Liming; McClean, S.I.; Redpath, J.
    It is generally assumed that all users in a dataset are equally adversely affected by data sparsity and hence addressing this problem should result in improved performance. However, although all users may be members of a sparse dataset, they do not all suffer equally from the data sparsity problem. This indicates that there is some ambiguity as to which users should be identified as suffering from data sparsity, referred to as sparse users throughout this paper, and targeted with new recommendation improvement strategies. This paper defines sparsity in terms of number of item ratings and average similarity with nearest neighbours and then goes on to look at the impact of sparsity so defined on performance. Counterintuitively, it is found that in top-N recommendations sparse users actually perform better than some other categories of users when a standard approach is used. These results are explained, and empirically verified, in terms of a bias towards users with a low number of ratings. The link between sparsity and performance is also considered in the case of predictions rather than top-N recommendations. This work provides the motivation for targeting improvement approaches towards distinct groups of users as opposed to the entire dataset.