Recommendation Method of SNS Following to Category Classification of Image and Text Information

이미지와 텍스트 정보의 카테고리 분류에 의한 SNS 팔로잉 추천 방법

  • 홍택은 (조선대학교 소프트웨어융합공학과) ;
  • 신주현 (조선대학교 제어계측로봇공학과)
  • Received : 2016.09.05
  • Accepted : 2016.09.26
  • Published : 2016.09.30

Abstract

According to many smart devices are development, SNS(Social Network Service) users are getting higher that is possible for real-time communicating, information sharing without limitations in distance and space. Nowadays, SNS users that based on communication and relationships, are getting uses SNS for information sharing. In this paper, we used the SNS posts for users to extract the category and information provider, how to following of recommend method. Particularly, this paper focuses on classifying the words in the text of the posts and measures the frequency using Inception-v3 model, which is one of the machine learning technique -CNN(Convolutional Neural Network) we classified image word. By classifying the category of a word in a text and image, that based on DMOZ to build the information provider DB. Comparing user categories classified in categories and posts from information provider DB. If the category is matched by measuring the degree of similarity to the information providers is classified in the category, we suggest that how to recommend method of the most similar information providers account.

다양한 스마트 디바이스의 발전에 따라 거리, 공간의 제약 없이 실시간으로 의사소통, 정보공유 등이 가능한 SNS(Social Network Service)를 즐기는 사용자(User)가 증가하고 있다. 의사소통, 관계 형성에 중점을 두었던 SNS 사용자들이 정보공유의 기능으로 SNS를 활용하는 추세이다. 본 논문에서는 사용자의 SNS 게시글을 이용하여 카테고리를 추출하고 정보제공자(Information provider)를 팔로잉 추천해주는 방법을 기술한다. 게시글의 텍스트에서 단어를 분류하고 빈도수를 측정하며, 머신 러닝 기법 중 하나인 CNN(Convolutional Neural Network)을 바탕으로 구축한 Inception-v3 모델을 이용하여 이미지를 단어로 분류한다. 텍스트와 이미지에서 분류한 단어를 DMOZ 기준으로 카테고리 분류하여 정보제공자 DB를 구축한다. 정보제공자 DB의 카테고리와 게시글에서 분류한 사용자의 카테고리를 비교한다. 카테고리가 일치할 경우 카테고리에 분류되어 있는 정보 제공자들를 대상으로 유사도를 측정하여 가장 비슷한 정보제공자의 계정을 추천해주는 방법에 대해 제안한다.

Keywords

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