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Sean M. McNee
Researcher at University of Minnesota
Publications - 27
Citations - 4874
Sean M. McNee is an academic researcher from University of Minnesota. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 13, co-authored 26 publications receiving 4483 citations. Previous affiliations of Sean M. McNee include University of Freiburg & FTI Consulting.
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Proceedings ArticleDOI
Improving recommendation lists through topic diversification
TL;DR: This work presents topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests, and introduces the intra-list similarity metric to assess the topical diversity of recommendation lists.
Proceedings ArticleDOI
Being accurate is not enough: how accuracy metrics have hurt recommender systems
TL;DR: This paper proposes informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies, and proposes new user-centric directions for evaluating recommender systems.
Proceedings ArticleDOI
Getting to know you: learning new user preferences in recommender systems
Al Mamunur Rashid,Istvan Albert,Dan Cosley,Shyong K. Lam,Sean M. McNee,Joseph A. Konstan,John Riedl +6 more
TL;DR: Six techniques that collaborative filtering recommender systems can use to learn about new users are studied, showing that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.
Proceedings ArticleDOI
On the recommending of citations for research papers
Sean M. McNee,Istvan Albert,Dan Cosley,Prateep Gopalkrishnan,Shyong K. Lam,Al Mamunur Rashid,Joseph A. Konstan,John Riedl +7 more
TL;DR: This paper investigated six algorithms for selecting citations, evaluating them through offline experiments and an online experiment to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks.
Proceedings ArticleDOI
Enhancing digital libraries with TechLens
TL;DR: This paper presents and experiments with hybrid recommender algorithms that combine collaborative filtering and content-based filtering to recommend research papers to users and shows that users value paper recommendations, that the hybrid algorithms can be successfully combined, and that these results can be applied to develop recommender systems for other types of digital libraries.