• Science of Emotion and Sensibility
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• v.20 no.1
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• pp.3-16
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• 2017

With the development and proliferation of three-dimensional(3D) printers, consumers in modern society can now print products of what they want three-dimensionally at home. However, consumers themselves would have to produce digital design maps that are compatible with 3D printers and to set up the optimum printing quality and temperature, as well as to pay for maintenance and repair of 3D printers and to respond to any possible lawsuits related to intellectual property right about designs in order to make possible consumer-tailored manufacturing through 3D printing. However, in reality, since it is very difficult for consumers to respond to these issues, it is necessary to develop services that perform 3D printing on behalf of consumers in the desired direction. Motivated by this objective, this study investigated user experiences on Shapeways(www.shapeways.com), which is a global online 3D printing product and sales companies, from many viewpoints in order to obtain insight into 3D printing services and modes which were preferred by consumers. The study result showed that quantitative evaluations on usability, search process, price adequacy, re-visit intention, diversity of design, and satisfaction of design was scored low overall. Furthermore, this study acquired insight about consumer-tailored 3D printing services through constructive suggestions on multi-language support, openness of manufacturing process, simultaneous operation of online and offline sites, design-oriented consumer-tailored manufacturing service, services that ensure delivery safety and product durability, and surface finishing services. This study is expected to provide a wide range of opinions not only on 3D printing service platform development but also on related industry and research.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.270-283
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• 2012

With the advent of multi-channel TV, IPTV and smart TV services, excessive amounts of TV program contents become available at users' sides, which makes it very difficult for TV viewers to easily find and consume their preferred TV programs. Therefore, the service of automatic TV recommendation is an important issue for TV users for future intelligent TV services, which allows to improve access to their preferred TV contents. In this paper, we present a recommendation model based on statistical machine learning using a collaborative filtering concept by taking in account both public and personal preferences on TV program contents. For this, users' preference on TV programs is modeled as a latent topic variable using LDA (Latent Dirichlet Allocation) which is recently applied in various application domains. To apply LDA for TV recommendation appropriately, TV viewers's interested topics is regarded as latent topics in LDA, and asymmetric Dirichlet distribution is applied on the LDA which can reveal the diversity of the TV viewers' interests on topics based on the analysis of the real TV usage history data. The experimental results show that the proposed LDA based TV recommendation method yields average 66.5% with top 5 ranked TV programs in weekly recommendation, average 77.9% precision in bimonthly recommendation with top 5 ranked TV programs for the TV usage history data of similar taste user groups.