• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.55-62
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• 2021

In this paper, we propose a human-content interface that allows users to quickly and efficiently search data through friction-based scrolling with ROI(Regions of interests). Our approach, conceived from the behavior of finding information or content of interest to users, efficiently calculates ROI for a given content. Based on the kernel developed by conceiving from GMM(Gaussian mixture model), information is searched by moving the screen smoothly and quickly to the location of the information of interest to the user. In this paper, linear interpolation is applied to make one softer inertia, and this is applied to scrolls. As a result, unlike the existing approach in which information is searched according to the user's input, our method can more easily and intuitively find information or content that the user is interested in through friction-based scrolling. For this reason, the user can save search time.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.369-373
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• 2022

In the decision-making domain, a document warehouse is designed to meet the analysis needs of users who may have a wide variety of analysis purposes. In this paper, we propose to integrate the preferences and interactions of users based on profiles to the concept of document warehouses. These profiles guarantee the integration of personalized documents and the collaborative recommendation of documents between different users sharing common interests.

• Journal of the East Asian Society of Dietary Life
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• v.16 no.6
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• pp.787-794
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• 2006

The purpose of this study was to investigate the interests in food and nutrition information of internet users, and find the major problems associated with the use of internet information. Of 660 internet users, including 152 professionals, 127 white-collars, 130 college students, and 29 others, 66.4% had accessed the internet to obtain information. Of the subjects, 71% had used the internet to obtain nutrition information, 91.3% of which answered that they would keep using the internet for this purpose. The area of greatest interest and need of the professional users, in relation to nutrition information from the internet, was 'recipe and menu' (p<0.001). Whereas, the white-collars were most interested in nutrition information for 'diet for weight control' and 'diet'(p<0.001). Students were most interested in nutrition information related to 'diet' and 'diet for weight control'(p<0.001). Due to the variety of interests and applications of different users, there is a need to develop specialized nutrition information sites for internet users to easily obtain reliable and valuable information.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.1-21
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• 2015

This paper aims to assess the feasibility of a new and less-focused type of online sociability (the watching network) as a useful information source for personalized recommendations. In this paper, we recommend scientific articles of interests by using the shared interests between target users and their watching connections. Our recommendations are based on one typical social bookmarking system, CiteULike. The watching network-based recommendations, which use a much smaller size of user data, produces suggestions that are as good as the conventional Collaborative Filtering technique. The results demonstrate that the watching network is a useful information source and a feasible foundation for information personalization. Furthermore, the watching network is substitutable for anonymous peers of the Collaborative Filtering recommendations. This study shows the expandability of social network-based recommendations to the new type of online social networks.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.191-212
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• 2012

Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user's interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning" capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user's preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user's time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.

• The Journal of Information Systems
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• v.23 no.1
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• pp.93-109
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• 2014

This study aims to develop a rich understanding of user goals in user-empowering information technologies which have been dominating part in the information systems environment. A particular focus is on users' goals in a social network site (SNS) which is a typical example of user-empowering technologies. Users conduct various activities in order to achieve diverse goals in SNS. Thus, investigating what goals users pursue in SNS will give insights into understanding the users. We employed the laddering interview technique and means-end chain approach. Interviews of 50 Facebook users were analyzed to produce a hierarchical goal map showing users' goal structure. The map contains 18 goals, including self-reflection, psychological stability, belongingness, improving productivity, and amusement as ultimate goals in SNS. In the map, there are varied routes from activities to ultimate goals in SNS; that is, a complex assembly consisting of activities and goals. The findings call the information systems research community to have more interests in diverse goals and values users seek with technologies.

• International Journal of Contents
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• v.14 no.3
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• pp.27-31
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• 2018

Social network service (SNS) data related to green tourism were used to estimate preferred tour sites and users' interests. Keywords related with green tour activities were employed to search the SNS data. SNS data were collected from Korean blogs such as Naver and Daum from June $1^{st}$ to August $31^{st}$ between 2015 and 2017 using text-mining solution. During the study period, seven hundred and five posts were analyzed. Associated words that frequently co-occurred with keywords were classified into different categories depending on the nature of associated words. Associated words included swimming pools and camping sites (location); experience and swimming pools (attribute); and water play and culture (culture/leisure). Our data suggest that SNS users with experience of green tourism in Korea exhibited interest in green tourism with swimming pools, camping sites, experience, water play and/or culture rather than particular popular sites. Based on the findings, it is recommended that preferred facilities such as swimming pools should be provided at green tourism sites to meet the users' needs and to facilitate green tourism.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.23-28
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• 2012

Recently, due to the increasing demand for mobile devices, mobile searching market is rapidly growing. However, there is the limit of screen size, when searching for mobile devices, various results should be shown at a glance. The reason is that results are important given that up to 43 percent of people tend to check only first page. In this paper, a set of keywords for searching will be used to find out the users' interests. Users were divided into groups after going through Collaboration filtering. Therefore, the result of this experiment, reduced time for searching and improved quality of searching were confirmed.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.618-627
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• 2014

Nowadays mobile users are using a popular service called Location-Based Services (LBS). LBS is very helpful for a mobile user in finding various Point of Interests (POIs) in their vicinity. To get these services, users must provide their personal information, such as user identity or current location, which severely risks the location privacy of the user. Many researchers are developing schemes that enable a user to use these LBS services anonymously, but these approaches have some limitations (i.e., either the privacy prevention mechanism is weak or the cost of the solution is too much). As such, we are presenting a robust scheme for mobile users that allows them to use LBS anonymously. Our scheme involves a client side application that interacts with an untrusted LBS server to find the nearest POI for a service required by a user. The scheme is not only efficient in its approach, but is also very practical with respect to the computations that are done on a client's resource constrained device. With our scheme, not only can a client anonymously use LBS without any use of a trusted third party, but also a server's database is completely secure from the client. We performed experiments by developing and testing an Android-based client side smartphone application to support our argument.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.140-149
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• 2018

Social data such as users' comments are unstructured in nature and up-to-date technologies for analyzing such data are constrained by the available storage space and processing time when fast storing and processing is required. On the other hand, it is even difficult in using a huge amount of dynamically generated social data to analyze the user features in a high speed. To solve this problem, we design and implement a topic association analysis system based on the latent Dirichlet allocation (LDA) model. The LDA does not require the training process and thus can analyze the social users' hourly interests on different topics in an easy way. The proposed system is constructed based on the Spark framework that is located on top of Hadoop cluster. It is advantageous of high-speed processing owing to that minimized access to hard disk is required and all the intermediately generated data are processed in the main memory. In the performance evaluation, it requires about 5 hours to analyze the topics for about 1 TB test social data (SNS comments). Moreover, through analyzing the association among topics, we can track the hourly change of social users' interests on different topics.