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http://dx.doi.org/10.6109/jkiice.2019.23.11.1321

Influencer Attribute Analysis based Recommendation System  

Park, JeongReun (Department of English Language and Literature, Ajou University)
Park, Jiwon (Department of English Language and Literature, Ajou University)
Kim, Minwoo (Department of Digital Media, Ajou University)
Oh, Hayoung (DASAN University College, Ajou University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.11, 2019 , pp. 1321-1329 More about this Journal
Abstract
With the development of social information networks, the marketing methods are also changing in various ways. Unlike successful marketing methods based on existing celebrities and financial support, Influencer-based marketing is a big trend and very famous. In this paper, we first extract influencer features from more than 54 YouTube channels using the multi-dimensional qualitative analysis based on the meta information and comment data analysis of YouTube, model representative themes to maximize a personalized video satisfaction. Plus, the purpose of this study is to provide supplementary means for the successful promotion and marketing by creating and distributing videos of new items by referring to the existing Influencer features. For that we assume all comments of various videos for each channel as each document, TF-IDF (Term Frequency and Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) algorithms are applied to maximize performance of the proposed scheme. Based on the performance evaluation, we proved the proposed scheme is better than other schemes.
Keywords
Influencer Attribute Analysis; Recommender System; TF-IDF; LDA;
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