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Neil Hurley
Researcher at University College Dublin
Publications - 170
Citations - 4536
Neil Hurley is an academic researcher from University College Dublin. The author has contributed to research in topics: Recommender system & Digital watermarking. The author has an hindex of 28, co-authored 165 publications receiving 4008 citations. Previous affiliations of Neil Hurley include Dublin City University & Trinity College, Dublin.
Papers
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Proceedings ArticleDOI
Avoiding monotony: improving the diversity of recommendation lists
Mi Zhang,Neil Hurley +1 more
TL;DR: This work model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem, leading to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution.
Journal ArticleDOI
Collaborative recommendation: A robustness analysis
TL;DR: This work analyzes the robustness of collaborative recommendation: the ability to make recommendations despite (possibly intentional) noisy product ratings, and formalizes recommendation accuracy in machine learning terms and develops theoretically justified models of accuracy.
Journal ArticleDOI
Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
Neil Hurley,Mi Zhang +1 more
TL;DR: It is argued that the motivation of diversity research is to increase the probability of retrieving unusual or novel items which are relevant to the user and a methodology to evaluate their performance in terms of novel item retrieval is introduced.
Proceedings Article
Detecting highly overlapping community structure by greedy clique expansion
TL;DR: GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities, and when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, the algorithm performs competitively.
Book ChapterDOI
Novelty and Diversity in Recommender Systems
TL;DR: An overview of the main contributions to this area in the field of recommender systems, and seeks to relate them together in a unified view, analyzing the common elements underneath the different forms under which novelty and diversity have been addressed, and identifying connections to closely related work on diversity in other fields.