• Journal of Korean Society for Geospatial Information Science
• /
• v.20 no.4
• /
• pp.67-75
• /
• 2012

For the last few years, LBS has attracted considerable attention from many industries and societies as a result of propagated smart devices. LBS has a high utilization of mobile users as it uses user positions as a significant factor. Current LBS has only taken user position into account and it makes some limits. So, it is necessarily suggested that support for personalized services which consider user's motion, traffic condition, weather condition, time, personal information and preferences that have a huge impact on the accuracy. The purpose of this study is to design the inference systems with user's motion, preferences and schedules and provide users with the personalized information. To achieve this, Movement Ontology, User Profile Ontology, Schedule Ontology and Work Ontology should be constructed and based on this, smart applications were developed. Developed applications induced appropriately recommended results according to user's preference, motion and directions.

• Journal of Broadcast Engineering
• /
• v.9 no.4 s.25
• /
• pp.334-344
• /
• 2004

The convergence of metadata service and the PDR with digital storage device enables new services. Among which, providing dynamic commercials make it possible for the commercials stored in the PDR to be a beneficial information source instead of a dull and not-so-interesting time-consuming content. It also improves consumer concentration and makes the commercials more effective. This paper provides list of various functionality provided by dynamic personalized commercials based on metadata and PDR. The proposed information structure supporting these functionality is based on the digital item concept of MPEG-2I. This paper also provides brief description on how the proposed information structure can be used to implement the listed functionality of dynamic persona1ized commercials.

• The KIPS Transactions:PartA
• /
• v.16A no.5
• /
• pp.389-394
• /
• 2009

Lately the usage of multimedia equipment with small LCD displays is rapidly increasing. Although many people use devices like this, videos intended for TV or HDTV are sent to these mobile devices. Therefore cases where it is hard for the user to view the desired scenes are growing more frequent. Currently, most services simply reduce the size of the content to fit the screen when they offer it for mobile devices. However, especially with sports broadcasts, there are many areas that cannot be seen very well because it was simply reduced in size. We therefore consider this weakness and are researching how to let the user choose an area of interest and then sending it to the user in a way that fits the device. In this paper, we address the problem of video delivery and personalization. For the delivered video content, we suggest the UP-SAM User Personalized Context-Aware Service Adaptation Middleware) model that uses the video content description and MPEG-21 multimedia framework.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.10
• /
• pp.117-124
• /
• 2021

In this paper, we propose CANVAS (Creative ANalytics enVironment And System), an analytics system of the National Research Data Platform (DataON). CANVAS is a personalized analytics cloud service for researchers who need computing resources and tools for research data analysis. CANVAS is designed in consideration of scalability based on micro-services architecture and was built on top of open-source software such as eGovernment Standard framework (Spring framework), Kubernetes, and JupyterLab. The built system provides personalized analytics environments to multiple users, enabling high-speed and large-capacity analysis by utilizing high-performance cloud infrastructure (CPU/GPU). More specifically, modeling and processing data is possible in JupyterLab or GUI workflow environment. Since CANVAS shares data with DataON, the research data registered by users or downloaded data can be directly processed in the CANVAS. As a result, CANVAS enhances the convenience of data analysis for users in DataON and contributes to the sharing and utilization of research data.

• Journal of Digital Convergence
• /
• v.19 no.7
• /
• pp.283-287
• /
• 2021

The purpose of this study is to analyze personalized contents and previews of OTT service based on user experience and to suggest the improvements of the preview viewing experience. Current domestic OTT mobile applications were researched to find out how services are offering content. Plus, the 20s-30s were recruited to analyze user experience. An online survey and in-depth interview were conducted by using Stephen P. Anderson's Creating Pleasurable Interface Model. As a result, preview help users to select content but it doesn't suit their taste. Also, the preview is hard to watch however they want. Therefore, it can be inferred that the preview requires the function for improving efficiency, preference, and accessibility. This study is expected to be used as research material on user experience or preview experience of OTT content.

• Journal of Digital Convergence
• /
• v.19 no.6
• /
• pp.299-308
• /
• 2021

This study aimed at identifying the significant effects and effectiveness of patients with mild dementia when using personalized reminiscence therapy digital services using AI voice technology. In the process of interpreting the results of stakeholder interviews, the design idea of personal customization using voice AI technology was derived, and prototypes were created and usability tests were conducted in the first and second rounds. The main results are as follows: Since reminiscence therapy itself is highly influenced by personal experience and can receive customized care guides based on treatment status and results through customized treatment programs, the concept of personalization can improve the quality of treatment than existing treatment methods. However, it is expected that the usability of the service will further increase if we study micro-interactions that can prevent errors and increase usability, as issues that may arise due to the forgetting cognitive characteristics of mild dementia patients are observed.

• Journal of the Korean Society for information Management
• /
• v.38 no.2
• /
• pp.113-127
• /
• 2021

The purpose of this study is to propose a personalized book recommendation system to promote the use of university libraries. In particular, unlike many recommended services that are based on existing users' preferences, this study proposes a method that derive evaluation metrics using individual users' book rental history and tendencies, which can be an effective alternative when users' preferences are not available. This study suggests models using two matrix decomposition methods: Singular Value Decomposition(SVD) and Stochastic Gradient Descent(SGD) that recommend books to users in a way that yields an expected preference score for books that have not yet been read by them. In addition, the model was implemented using a user-based collaborative filtering algorithm by referring to book rental history of other users that have high similarities with the target user. Finally, user evaluation was conducted for the three models using the derived evaluation metrics. Each of the three models recommended five books to users who can either accept or reject the recommendations as the way to evaluate the models.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.9
• /
• pp.59-68
• /
• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.4
• /
• pp.161-175
• /
• 2024

In this paper, we examined the causes of privacy concern and related factors in personalized recommendation service of OTT. On the basis of the 'Big Five Personality model,' we established factors such as agreeableness, neuroticism, conscientiousness, extraversion, and openness to experience. Additionally, we established factors such as accuracy, diversity, and novelty of OTT recommendation's services, and perceived transparency. we analyzed the relationship between privacy concern, service benefit, and intention to give personal information. Finally, we analyzed the mediating effect of service benefits on the relationship between privacy concern and intention to give personal information. The results of this study showed that (1) neuroticism, extraversion and openness to experience had the significant effects on privacy concerns, (2) perceived transparency had the significant effects on privacy concern, 3) privacy concern and service benefit had the significant effect on intention to give personal information, and (4) as a result of multi-group analysis towards low and high groups to verify the moderating effect by service benefits, a significant difference was observed between privacy concern and intention to give personal information. The findings of the study are expected to help the OTT firms' understanding towards users' privacy protection behaviors.

• Journal of the Korea Convergence Society
• /
• v.7 no.6
• /
• pp.49-54
• /
• 2016

The IoT has attracted attention as one of the technologies that are applied to various industries and create new services. The IoT can utilize existing network technologies to create services by providing Internet connection between objects. Objects Personalized services can be created by collecting various data using the IoT. In the field of agriculture, we are promoting sustainable agriculture and enhancing competitiveness through the use of the IoT, and the convergence of IoT in agriculture is pushing for smart agriculture. In Korea, the Ministry of Agriculture, Food and Rural Affairs is preparing measures to spread smart farms to improve agricultural competitiveness using IoT technology. Therefore, we propose the development model of smart agriculture in the future through the case study on the IoT based on agriculture.