  In this paper, we propose a new measure to estimate the similarity between
brands via posts of brands' followers on social network services (SNS). Our
method was developed with the intention of exploring the brands that customers
are likely to jointly purchase. Nowadays, brands use social media for targeted
advertising because influencing users' preferences can greatly affect the
trends in sales. We assume that data on SNS allows us to make quantitative
comparisons between brands. Our proposed algorithm analyzes the daily photos
and hashtags posted by each brand's followers. By clustering them and
converting them to histograms, we can calculate the similarity between brands.
We evaluated our proposed algorithm with purchase logs, credit card
information, and answers to the questionnaires. The experimental results show
that the purchase data maintained by a mall or a credit card company can
predict the co-purchase very well, but not the customer's willingness to buy
products of new brands. On the other hand, our method can predict the users'
interest on brands with a correlation value over 0.53, which is pretty high
considering that such interest to brands are high subjective and individual
dependent.
