International Conference on Context-Aware Systems and Applications

Context-Aware Systems and Applications pp 101-110

MBTI-Based Collaborative Recommendation System: A Case Study of Webtoon Contents

Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 165)

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.

Keywords

Webtoon Recommendation MBTI (Myers-Briggs Type Indicator) Collaborative filtering 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringChung-Ang UniversitySeoulSouth Korea

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