Research Article
MBTI-Based Collaborative Recommendation System: A Case Study of Webtoon Contents
@INPROCEEDINGS{10.1007/978-3-319-29236-6_11, author={Myeong-Yeon Yi and O-Joun Lee and Jason Jung}, title={MBTI-Based Collaborative Recommendation System: A Case Study of Webtoon Contents}, proceedings={Context-Aware Systems and Applications. 4th International Conference, ICCASA 2015, Vung Tau, Vietnam, November 26-27, 2015, Revised Selected Papers}, proceedings_a={ICCASA}, year={2016}, month={4}, keywords={Webtoon Recommendation MBTI (Myers-Briggs Type Indicator) Collaborative filtering}, doi={10.1007/978-3-319-29236-6_11} }- Myeong-Yeon Yi
O-Joun Lee
Jason Jung
Year: 2016
MBTI-Based Collaborative Recommendation System: A Case Study of Webtoon Contents
ICCASA
Springer
DOI: 10.1007/978-3-319-29236-6_11
Abstract
A large number of Webtoon contents has caused difficulties on finding relevant Webtoons for users. Thereby, an efficient recommendation services are needed. However, since the existing recommendation method (e.g. collaborative filtering) has two fundamental problems: (i.e., data sparsity and scalability problem), it has difficulties with reflecting users’ personality. In this paper, we propose the MBTI-CF method to solve these problems and to involve users’ personality by building personality-based neighborhood using MBTI. In order to verify the efficiency of the proposed method, we conducted statistical testing by user survey (anonymous users have rated set of the pre-selected Webtoon contents). Three experimental results have shown that MBTI-CF provides improvement in terms of the data sparsity problem and the scalability problem and offers more stable performance.