• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.321-326
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• 2019

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

• Knowledge Management Research
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• v.11 no.2
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• pp.75-93
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• 2010

Even though there have been many efforts on driver's route recommendation, driver still should get involved to choose the driving path in a manual manner. Uncertain traffic information provided to the driver delays his arrival time and hence may cause diminished economic values. One of the solutions of reducing the uncertainty is to provide various kinds of traffic information, rather than send real-time information. Therefore, as the wireless communication technology improves and at the same time volume of utilizable traffic contents increases in geometrical progression, selecting traffic information based on driver's context in a timely and individual manner will be needed. Hence, the purpose of this paper is to propose a methodology that efficiently sends the rich traffic contents to the personal in-vehicle navigation. To do so, driver preference is modeled and then the recommendation algorithm of traffic information contents was developed using the preference model. Secondly, ontology based traffic situation analyzation method is suggested to automatically inference the noticeable information from the traffic context on driver's route. To show the feasibility of the idea proposed in this paper, an open API service is implemented in consideration of ease of use.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1499-1511
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• 2022

In the context of globalization, circulation between cities has become more frequent. The airport is no longer just a place for boarding, disembarking, and transportation, but a public place that serves as the communication function of the "aviation city". The intervention of public art in the airport space not only gives users a sense of space experience, but also becomes a unique carrier for city and country image shaping. The purpose of this paper is to study the emotional value brought by airport public art to users, and to investigate the correlation analysis of public art design elements and user preferences based on this premise. The research methods are machine learning method and SPSS 21.0. The user's emotional value is introduced in the big data evaluation, and the preference and inclination of airport users to various elements of public art are analyzed by questionnaire. Through the research conclusion, the preference and main contradiction of users in the airport for the four dimensions of public art design elements are obtained. Opinions and optimization methods to provide reference data and theoretical support for public art design.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.94-101
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• 2000

User adaptive reception of broadcast programs includes the functionality such as the user adaptive filtering and browsing functionality. The user adaptive filtering means that the user can limit the list of programs to include only his/her favorite programs among hundreds of available programs. The user adaptive browsing means that the user can view a short summary of his/her selection in the way that he/she prefers. When the receiving system include the random access storage device, the automatic recording functionality of users favorite programs can be included. The user adaptive reception requires support from various meta-data such as user preference data and content description data. TV Anytime forum is a standardization effort to enable user adaptive TV reception, which means that the user can watch what s/he wants when s/he want in the way s/he wants. MPEG-7 includes not only the content description for broadcast applications but also other content descriptions such as structure information. This paper addresses the relationship between MPEG-7 and TV Anytime and investigates how MPEG-7 should be designed and be used to satisfy the requirements of the user adaptive reception of broadcast program.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.122-129
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• 2014

Effective information gathering and retrieval of the most relevant web documents on the topic of interest is difficult due to the large amount of information that exists in various formats. Current information gathering and retrieval techniques are unable to exploit semantic knowledge within documents in the "big data" environment; therefore, they cannot provide precise answers to specific questions. Existing commercial big data analytic platforms are restricted to a single data type; moreover, different big data analytic platforms are effective at processing different data types. Therefore, the development of a common big data platform that is suitable for efficiently processing various data types is needed. Furthermore, users often possess more than one intelligent device. It is therefore important to find an efficient preference profile construction approach to record the user context and personalized applications. In this way, user needs can be tailored according to the user's dynamic interests by tracking all devices owned by the user.

• CRM연구
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• v.4 no.1
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• pp.55-68
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• 2011

Recently with the development of Web services by utilizing a variety of web content, the studies on user experience and personalization based on web usage has attracted much attention. Majority of personalized analysis are have been carried out based on existing data, primarily using the database and statistical models. These approaches are difficult to reflect in a timely mannerm, and are limited to reflect the true behavioral characteristics because the data itself was just a result of customers' behaviors. However, recent studies and commercial products on web analytics try to track and analyze all of the actions from landing to exit to provide personalized service. In this study, by analyzing the customer's click-stream behaviors, we define U-Score(Usage Score), P-Score (Preference Score), M-Score(Mania Score) to indicate variety of customer preferences. With the devised three indicators, we can identify the customer's preferences more precisely, provide in-depth customer reports and customer relationship management, and utilize personalized recommender services.

• Journal of the Korea Furniture Society
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• v.27 no.3
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• pp.226-236
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• 2016

Single-person household is estimated to be about 26.5% of the total household in 2015, which counts as 5060000 in numbers. We opt to acknowledge the various requests of these single residents, and in order to raise their satisfaction, we investigated on how personal taste, psychological interest, and personality attribute affects the user's preference of space organization and furniture in one room housing. Using the qualified psychology program, Enneagram Personality Type Indicator, we surveyed young people under 30 years old (majority of single-person households), regarding space organization and furniture preference. With the help of a specialist, the survey was constructed with appropriate evaluation items (space organization in one room households, bed, sofa, furniture material, etc), and analyzed the relationship between the evaluated items and personality types. Results showed there is a relationship between personality types and spatial structure. First, preference of spatial structure differed for different personality types. Second, the shape and size of furniture was dependent more on the ease of usability and design rather than on the personality types. One thing to consider is that type 1 and 9 accounted for about 50% of the total surveys. This emphasizes that the preferred spatial structure of a dominant specific personality type should not be overlooked.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.1-6
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• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.716-720
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• 2006

Fuzzy sets are good for abstracting and unifying information using natural language like terms. However, fuzzy sets embody vagueness and users may have different attitude to the vagueness, each user may choose difference one as the best among several fuzzy values. In this paper, we develop a method teaming a user's, preference on fuzzy values and select one which fits to his preference. Users' preferences are modeled with artificial neural networks. We gather learning data from users by asking to choose the best from two fuzzy values in several representative cases of comparing two fuzzy sets. In order to establish tile representative comparing cases, we enumerate more than 600 cases and cluster them into several groups. Neural networks ate trained with the users' answer and the given two fuzzy values in each case. Experiments show that the proposed method produces outputs closet to users' preference than other methods.