• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.23 no.4
• /
• pp.283-308
• /
• 2012

With explosive growth of information by recent advancements in information technology and the Internet, users need a method to acquire appropriate information. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Also, users and service providers are growing more and more interested in personalized information recommendation. This study designed and implemented personalized information recommendation system based on AR as a method to provide positive information service for information users as a method to provide positive information service. To achieve the goal, the proposed method overcomes the weaknesses of existing systems, by providing a personalized recommendation method for contents that works in a large-scaled data and user environment. This study based on the proposed method to extract rules from log files showing users' behavior provides an effective framework to extract Association Rule.

• Information Systems Review
• /
• v.17 no.1
• /
• pp.93-115
• /
• 2015

Due to the dramatic increase in usage of smart phones, mobile applications offer a promising public communication channel to effect behavioral change in areas such as energy saving. For this, identifying relationships between applications and behavioral change would be useful for public institutions to formulate more effective public communication strategies. Based on a design science approach, this study examines the effect of design factors of public service-oriented mobile applications such as personalization, accessibility, and representation richness on users' cognitive and affective attitudes. Their effects on behavioral outcome are also investigated. The results of the analysis show that most design factors contribute significantly to the cognitive and affective attitudes of users. In turn, cognitive attitudes affect users' behavioral change in terms of energy saving, while affective attitudes are positively associated with the average usage time of mobile applications and behavioral changes in terms of energy saving.

• Journal of the Korea Computer Industry Society
• /
• v.2 no.9
• /
• pp.1175-1182
• /
• 2001

One to one Marketing (a.k.a. database marketing or relationship marketing) is one of the many fields that will benefit from the electronic revolution and shifts in consumer sales and advertising. As a component of intelligent customer services on Internet storefront, this paper describes technology of providing personalized advertisement using the market basket analysis, a well-Known data mining technique. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customer's data. However, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed Knowledge base. In this paper, using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store.

• Journal of Korea Multimedia Society
• /
• v.13 no.10
• /
• pp.1413-1422
• /
• 2010

With the emergence of digital TV broadcasting, various channels are provided to a TV audience. But it is getting hard for the audience to find his or her preferred TV programs due to the huge number of TV channels. In order to mitigate the difficulty, TV broadcasting companies provide an electronic program guide (EPG), which is a digital guide to scheduled broadcast TV programs. However, it results in the information overload problem and the time-consuming problem since the number of TV channels and programs is gradually on the increase. In this paper, we design and develop a channel filtering system, which recommends a small number of channels by filtering TV channels based on the watching pattern of the TV audience. The channel filtering system does not require the replacement or upgrade of existing TV or set-top box. In addition, it increases usability by skipping the channels that broadcast the audience's non-preferred TV programs while the TV audience presses the channel up/down button.

• Journal of Advanced Navigation Technology
• /
• v.18 no.6
• /
• pp.588-593
• /
• 2014

Recently, the smart devices has been possessed by a large majority of the adult, and offered various personalization services. This paper proposed the lightweight Intelligent Image Security System that notice the existence of any intruder in real time at the place of requiring the security by using smart device. The proposed image security system recognized whether or not intruder exists using the difference frame on the basis of Integral Image and the dynamic background updating algorithms. The intrusion notification is achieved by using the GCM push server that send massages in the application unit of user mobile device, and the SMTP mail server which is use of e-mail standard protocol. In case of the occurrence of intruder, GCM push server send an push-massage by the private mobile device, and SMTP mail server send the intruder's photograph and intrusion time. By the convergence of the various image processing algorithms and the performance of smart device, The proposed image security system can be applied to the various Intelligent Image Security field.

• The KIPS Transactions:PartD
• /
• v.9D no.6
• /
• pp.1009-1016
• /
• 2002

We design and implement a new case-based recommender system using implicit rating information for a digital content site. Our system consists of the User Profile Generation module, the Similarity Evaluation and Recommendation module, and the Personalized Mailing module. In the User Profile Generation Module, we define intra-attribute and inter-attribute weight deriver from own's past interests of a user stored in the access logs to extract individual preferences for a content. A new similarity function is presented in the Similarity Evaluation and Recommendation Module to estimate similarities between new items set and the user profile. The Personalized Mailing Module sends individual recommended mails that are transformed into platform-independent XML document format to users. To verify the efficiency of our system, we have performed experimental comparisons between the proposed model and the collaborative filtering technique by mean absolute error (MAE) and receiver operating characteristic (ROC) values. The results show that the proposed model is more efficient than the traditional collaborative filtering technique.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.9
• /
• pp.615-620
• /
• 2013

With the development of smartphones, the number of applications for smartphone increases sharply. As a result, users need to try several times to find their favorite apps. In order to solve this problem, we propose a recommendation system to provide an appropriate app list based on the user's log information including time stamp, location, application list, and so on. The proposed approach learns three recommendation models including Naive-Bayesian model, SVM model, and Most-Frequent Usage model using temporal and spatial attributes. In order to figure out the best model, we compared the performance of these models with variant features, and suggest an hybrid method to improve the performance of single models.

• Journal of KIISE:Computer Systems and Theory
• /
• v.36 no.6
• /
• pp.488-501
• /
• 2009

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In order to provide such services, we must have mobile devices that connect users and services. But such services are usually limited to those served on a single mobile device. To resolve the resource limitation problem of mobile devices, a nearby resource sharing research has been studied. Also, not only the nearby resource share but also a resource recommendation through context-based resource reasoning has been studied such as an UMO Project. The UMO Project share and manage the various context information for the personalization resource recommendation and reason based on current context information. Also, should verify resource inference rules for reliable the resource recommendation. But, to create various context information requires huge cost and time in actuality. Thus, we propose a inference verification tool called USim to resolve problem. The proposed inference verification tool provides convenient graphic user interfaces and it easily creates context information. The USim exactly verifies new inference rules through dynamic changes of context information.

• Survey Research
• /
• v.3 no.2
• /
• pp.91-122
• /
• 2002

The use of internet is expanding globally in a rapid way. The situation is similar in Korea. The growth of internet affects the various aspects of a social system. Such an impact is not an exception in the field of survey data-collection. The internet survey method has gained a recognition as an important survey approach in the U.S and Europe. A number of Korean research organizations have already started implementing the new survey method, too. The time is quite ripe for the internet survey in Korea. However, it is hard to find ever a good literature review on the subject. This study intends to review the results of the past studies about the question of what factors influence the response rate, speed, and quality. The influencing factors incorporated in this review include six elements: personalization, the survey sponsorship, incentives, the questionaire format, prenotification, and follow-ups. Regarding there sis factors, the results of the past studies relating to the internet survey response are reviewed in a systematic way.

• Journal of the Korean Society for Library and Information Science
• /
• v.45 no.4
• /
• pp.77-102
• /
• 2011

The purpose of this study is to design, implement and evaluate a personalized search system using gathered information on users to provide more accurate search results. For this purpose, a hybrid-based user profile is constructed by using subject classification. In order to evaluate the performance of the proposed system, experts directly measured and evaluated MRR, MAP and usability by using the Korean journal articles of science and technology DB. Its performance was better than the general search system in the area of "Computer Science" and "Library and Information Science". Especially better results were shown when tested on ambiguous keywords. Evaluation through in-depth interviews proved that the proposed personalized search system was more efficient in looking up and obtaining information. In addition, the proposed personalized search system provided a variety of recommendation systems which proved helpful in navigating for new information. High user satisfaction ratings on the proposed personalized search system were another proof of its usefulness. In this study, we were able to prove through expert evaluation that the proposed personalized search system was more efficient in information retrieval.