K
Keunho Choi
Researcher at Korea University
Publications - 21
Citations - 556
Keunho Choi is an academic researcher from Korea University. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 9, co-authored 19 publications receiving 484 citations.
Papers
More filters
Journal ArticleDOI
A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis
TL;DR: It is contended that implicit rating can successfully replace explicit rating in CF and that the hybrid approach of CF and SPA is better than the individual ones.
Journal ArticleDOI
A new similarity function for selecting neighbors for each target item in collaborative filtering
Keunho Choi,Yongmoo Suh +1 more
TL;DR: The objective of this paper is to propose a new similarity function in order to select different neighbors for each different target item, and experimental results from MovieLens dataset and Netflix dataset provide evidence that the proposed recommender model considerably outperforms the traditional CF-based recommender models.
Journal ArticleDOI
CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns
TL;DR: This paper developed a model which classifies customers into VIP or non-VIP, using various data mining techniques such as decision tree, artificial neural network, logistic regression and bagging as a base classifier, and identified association rules and sequential patterns from the transactions of VIPs.
Journal ArticleDOI
Classification cost: An empirical comparison among traditional classifier, Cost-Sensitive Classifier, and MetaCost
TL;DR: This paper compares the classification cost incurred by a traditional cost-insensitive classification approach and two cost-sensitive classification approaches, Cost-Sensitive Classifier (CSC) and MetaCost, and shows that the lowest classification cost was incurred when the MetaCost approach was used and when non-fraud data and fraud data were balanced.
Journal ArticleDOI
Recommending valuable ideas in an open innovation community: A text mining approach to information overload problem
TL;DR: Using the proposed system, the firm can get recommendations for ideas that could be valuable for its business innovation in the idea generation phase, thereby resolving the information overload and enhancing the effectiveness of open innovation.