With the development of various smart devices, an increasing number of people are using SNS, which enables communication and sharing of information regardless of distance and place. Users who used to focus on the functions of forming relationship and communication are now using SNS for the function of sharing information. The existing way of recommending following was mostly based on relationship so there is the limitation of recommending following for users who use SNS for information. Also, as studies using SNS postings mostly suggest ways of categorizing interests and recommending following based on images and texts, it is hard to understand users’ intention exactly.
So this paper suggests the way of recommending information-centered following that finds out users’ interests by using images and texts and categorizing interests of users. This study secured objectivity by defining DOMZ, which is established as ODP(Open Directory Project), as the standard for category of interest, and by using CNN(Convolutional Neuronal Network), one of the machine learning methods, it studied images and text and divided interests into categories. It defined users who post many postings as information providers, established information database and categorized users with the same interests. Then it measured the similarity of the distance between information providers and users and recommended the information provider with the most similar interests for following.
As a result of categorizing interests of information providers and of users using the method suggested in this paper, the rate of accuracy was 80% and 79.8% respectively, and the overall rate of accuracy was 79.93%, which means a good categorization overall. Categories of interest with low rate of accuracy are thought to be because the meanings were similar and similar data was collected when images and texts were searched. There seems to be a slight correlation between categories. Therefore, it seems that there need to be studies on the selection of categories and standards for making SNS categories, and also studies on establishing proper CNN models to learn and categorize texts that are made up of special characters and emoticons due to the nature of texts of SNS postings.
What can be suggested is to divide users’ interests into 16 categories then recommend following in order to recommend more proper following for information-focused SNS users, and by using images and texts together, it was possible to accurately understand the intention of users. Through the way suggested by this paper, it is likely that it will be possible to recommend following that is suitable for SNS users, and categorization of interests will be used in customized services, which recommend contents and information suitable for users’ interests, and SNS marketing services.