H
Heung-Nam Kim
Researcher at University of Ottawa
Publications - 46
Citations - 1074
Heung-Nam Kim is an academic researcher from University of Ottawa. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 15, co-authored 46 publications receiving 1004 citations. Previous affiliations of Heung-Nam Kim include Inha University.
Papers
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Journal ArticleDOI
Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation
TL;DR: Experimental results show that the proposed algorithm offers significant advantages both in terms of improving the recommendation quality for sparse data and in dealing with cold-start users as compared to existing work.
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Collaborative error-reflected models for cold-start recommender systems
TL;DR: This paper proposes a unique method of building models derived from explicit ratings and applies the models to CF recommender systems, and shows significant improvement in dealing with cold start problems, compared to existing work.
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Collaborative user modeling with user-generated tags for social recommender systems
TL;DR: By leveraging user-generated tags as preference indicators, a new collaborative approach to user modeling that can be exploited to recommender systems is proposed that provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.
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A group trust metric for identifying people of trust in online social networks
TL;DR: This study proposes the extended Advogato trust metric that facilitates the identification of trustworthy users associated with each individual user and presents the capacity-first maximum flow method capable of finding the strongest path pertinent to discovering an ordered set of reliable users and preventing unreliable users from accessing personal networks.
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Collaborative user modeling for enhanced content filtering in recommender systems
TL;DR: This study proposes a collaborative approach to user modeling for enhancing personalized recommendations to users that first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users.