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YoungOk Kwon
Researcher at Sookmyung Women's University
Publications - 13
Citations - 1522
YoungOk Kwon is an academic researcher from Sookmyung Women's University. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 6, co-authored 12 publications receiving 1318 citations. Previous affiliations of YoungOk Kwon include University of Minnesota.
Papers
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Journal ArticleDOI
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
TL;DR: A number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy are introduced and explored.
Journal ArticleDOI
New Recommendation Techniques for Multicriteria Rating Systems
TL;DR: This article proposes several new techniques for extending recommendation technologies to incorporate and leverage multicriteria rating information and improve recommendation accuracy as compared with single-rating recommendation approaches.
Book ChapterDOI
Multi-Criteria Recommender Systems
TL;DR: This chapter aims to provide an overview of the class of multi-criteria recommender systems, i.e., the category ofRecommender systems that use multi-Criteria preference ratings, with a discussion on open issues and future challenges for the class.
Journal ArticleDOI
Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity
TL;DR: Several optimization-based approaches for improving aggregate diversity of top- N recommendations are proposed, including a greedy maximization heuristic, a graph-theoretic approach based on maximum flow or maximum bipartite matching computations, and an integer programming approach.
Proceedings Article
Towards more confident recommendations: Improving recommender systems using filtering approach based on rating variance
TL;DR: This paper proposes several new approaches to improve the accuracy of recommender systems by using rating variance to gauge the confidence of recommendations, and empirically demonstrates how these approaches work with various recommendation techniques.