• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.461-468
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• 2018

Developers spend a significant amount of time exploring and trying to understand source code to find a source location to modify. To reduce such time, existing studies have recommended the source location using statistical language model techniques. However, in these techniques, the recommendation does not occur if input data does not exactly match with learned data. In this paper, we propose a code location recommendation method using Recurrent Neural Networks and interaction histories, which does not have the above problem of the existing techniques. Our method achieved an average precision of 91% and an average recall of 71%, thereby reducing time for searching and exploring code more than the existing recommendation techniques.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.64-75
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• 2018

With the development of location-aware technologies and the activation of smart phones, location based social networks(LBSN) have been activated to allow people to easily share their location. In particular, studies on recommending the location of user interests by using the user check-in function in LBSN have been actively conducted. In this paper, we propose a location recommendation scheme considering time and user activities in LBSN. The proposed scheme considers user preference changes over time, local experts, and user interest in rare places. In other words, it uses the check-in history over time and distinguishes the user activity area to identify local experts. It also considers a rare place to give a weight to the user preferred place. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Database Research
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• v.34 no.3
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• pp.86-98
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• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.260-270
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• 2019

As the growth of developing Islamic countries, Muslims are into the world. The most important thing for Muslims to purchase food, ingredient, cosmetics and other products are whether they were certified as 'Halal'. With the increasing number of Muslim tourists and residents in Korea, Halal restaurants and markets are on the rise. However, the service that provides information on Halal restaurants and markets in Korea is very limited. Especially, the application of recommendation system technology is effective to provide Halal restaurant information to users efficiently. The profiling of Halal restaurant information should be preceded by design of recommendation system, and design of recommendation algorithm is most important part in designing recommendation system. In this paper, an Intelligent Recommendation Service system for offering Halal food (IRSH) based on dynamic profiles was proposed. The proposed system recommend a customized Halal restaurant, and proposed recommendation algorithm uses hybrid filtering which is combined by content-based filtering, collaborative filtering and location-based filtering. The proposed algorithm combines several filtering techniques in order to improve the accuracy of recommendation by complementing the various problems of each filtering. The experiment of performance evaluation for comparing with existed restaurant recommendation system was proceeded, and result that proposed IRSH increase recommendation accuracy using Halal contents was deducted.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.375-381
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• 2019

Recently, the recommendation system has attracted the attention of users as customized recommendation services have been provided focusing on fashion, video and music. But these services are difficult to provide users with proper service according to many different contexts because they do not use contextual information emerging in real time. When applied contextual information expands dimensions, it also increases data sparsity and makes it impossible to recommend proper music for users. Trying to solve these problems, our study proposed a music recommendation system to recommend proper music in real time by applying association rules and using relationships and rules about the current location and time information of users. The accuracy of the recommendation system was measured according to location and time information through 5-fold cross validation. As a result, it was found that the accuracy of the recommendation system was improved as contextual information accumulated.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.47-57
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• 2015

Recently, Internet of Things (IoT) have become popular for research and development in many areas. IoT makes a new intelligent network between things, between things and persons, and between persons themselves. Social network service technology is in its infancy, but, it has many benefits. Adjacent users in a social network tend to trust each other more than random pairs of users in the network. In this paper, we propose recommendation technique using social network in Internet of Things environment. We study previous researches about information recommendation, IoT, and social IoT. We proposed SIoT_P(Social IoT Prediction) using social relationships and item-based collaborative filtering. Also, we proposed SR(Social Relationship) using four social relationships (Ownership Object Relationship, Co-Location Object Relationship, Social Object Relationship, Parental Object Relationship). We describe a recommendation scenario using our proposed method.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.707-718
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• 2007

Recently, much studies for providing mobile users with suitable and useful content services, LBS(Location Based Service) corresponding to the change of users' location, are actively going on. First and foremost, this is basically owing to the progress of location management technologies such as GPS, mobile communication technology and the spread of personal devices like PDA and the cellular phones. Besides, the research scope of LBS has been changed from vehicle tracking and navigation services to intelligent and personalized services considering the changing information of conditions or environment where the users' are located. For example, it inputs the information such as heavy traffic, pollution, and accidents. The query languages which effectively search the stored vehicle and environment information have been studied depending on the increase of the information utilization. However, most of existing moving object query languages are not enough to provide a recommendation service for a user, because they can not be tested and evaluated in real world and did not consider changed environment information. In order to retrieve not only a vehicle location and environment condition but also use them, we suggest a moving object query language for recommendation service and implement a moving object query process system for supporting a query language. It can process a nearest neighbor query for recommendation service which considers various attributes such as a vehicle's location and direction, environment information. It can be applied to location based service application which utilizes the recommended factors based on environmental conditions.

• ETRI Journal
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• v.29 no.2
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.322-329
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• 2017

Recently, the importance of personalized healthcare(wellness) services is increasing in the era of the 4th Industrial Revolution. However, the authoring of wellness contents fused with variety of contents and the study of the system which provides the customized recommendation are insufficient. In this paper, we proposes the recommendation service system for the customized convergence wellness contents. The proposed system makes to the wellness contents by the existing cultural/tourism/leisure contents and recommends the customized wellness contents based on a user's profile and the situation information such as location and weather. The proposed systems is expected to contribute to designing the innovative and new service models for the tailored wellness content.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.7-14
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• 2008

One of purposes of installing signals at intersections is to protect traffic conflicts and accidents from occurring by means of arranging the right-of-way of travel more clearly. On the other hand, the installation of signals, and especially their location, can also have negative effects on safety. Therefore, the location of signals is of great importance. To secure a high safety level for urban signalized intersection, efforts are required to introduce a comprehensive recommendation or guideline for safety aspects of signal installation that takes local conditions into account. In this context, this reports on a study that analyzed the influence of signal location on the behavior of drivers who approach a signalized intersection in urban area. As a result, the study found out that the traffic signal location strongly affects the braking point of the Dilemma Zone(DZ), and the braking point of the DZ based on driving speed. Also, in terms of design layout, it has been illustrated that there is a close relation between signal location and road safety, especially DZ safety. Finally, this paper proposes a practical recommendation for signal installation related to how to locate the signal in practice for the sake of securing the safety level of signalized intersection.