• Title/Summary/Keyword: 이용자 세그멘테이션

Search Result 3, Processing Time 0.015 seconds

Understanding information users through user segmentation using factor analysis and cluster analysis (요인 분석과 클러스터 분석 기법을 활용한 사용자 세분화를 통한 정보이용자 이해)

  • Park, Minsoo
    • The Journal of the Convergence on Culture Technology
    • /
    • v.6 no.3
    • /
    • pp.437-442
    • /
    • 2020
  • Since the advent of the innovative information technology called the Internet, the dynamism of the information environment has brought about changes in information users' needs and behavior. It is essential to understand information users in this rapidly changing environment, and based on this, it is necessary to effectively build and operate an information service and a system therefor. The purpose of this study is to understand the characteristics according to the segmentation of users of the National Science and Technology Information Service System, and to derive improvements to customized services and content development through research and analysis of content usage. A total of 816 science and technology information service system users participated in online surveys from September to November. Collected data is applied to factor analysis and cluster analysis techniques to subdivide users of science and technology information service systems, to recognize new information technologies and information services, science technology information needs, and science and technology attributes that users consider important. We derived the results according to the segmented user group.

A proposition on digital maniac consumer market segmentation by consumer characteristics and behavior (매니아 소비자의 태도와 성향에 의한 디지털 매니아 세그멘테이션 제안)

  • Kim, You-Rie;Lee, Hye-Sun
    • Archives of design research
    • /
    • v.19 no.5 s.67
    • /
    • pp.243-254
    • /
    • 2006
  • Maniac consumers have a ripple effect on marketing because they are the main body of trends and consumer economy. So It is very important that we should first read needs and wants - in other words, psychological motives. And then we should find maniac consumer segments. This is an exploratory study that was done to obtain an insight for the new maniac consumer market segmentation. It examined the definition and characteristics of digital maniacs in Korea, and it carried out a literature study on consumers who have a similar consumption trend as the maniac users as a pre-study. Also, it looked into the trends and values of the maniac community in Korea, using the previous study's scale for innovative consumers. Next, the study interviewed maniac users using the first data and focused on discovering and grouping the new maniac segments based on the results. The study analyzed the purchase behaviors, decision-making, attitude for involvement and potential needs of the digital maniacs in Korea, and it discovered the segments for the segmentation of maniacs so it could find out the disposition and status of the digital maniacs. Such approach can be used as a strategical due for maniac target marketing and design(customer-oriented marketing and design) in the future.

  • PDF

Image recommendation algorithm based on profile using user preference and visual descriptor (사용자 선호도와 시각적 기술자를 이용한 사용자 프로파일 기반 이미지 추천 알고리즘)

  • Kim, Deok-Hwan;Yang, Jun-Sik;Cho, Won-Hee
    • The KIPS Transactions:PartD
    • /
    • v.15D no.4
    • /
    • pp.463-474
    • /
    • 2008
  • The advancement of information technology and the popularization of Internet has explosively increased the amount of multimedia contents. Therefore, the requirement of multimedia recommendation to satisfy a user's needs increases fastly. Up to now, CF is used to recommend general items and multimedia contents. However, general CF doesn't reflect visual characteristics of image contents so that it can't be adaptable to image recommendation. Besides, it has limitations in new item recommendation, the sparsity problem, and dynamic change of user preference. In this paper, we present new image recommendation method FBCF (Feature Based Collaborative Filtering) to resolve such problems. FBCF builds new user profile by clustering visual features in terms of user preference, and reflects user's current preference to recommendation by using preference feedback. Experimental result using real mobile images demonstrate that FBCF outperforms conventional CF by 400% in terms of recommendation ratio.