• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.309-316
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• 2018

The main purpose of this study is to understand how cognitive factors influence the way people interact with information/information systems, by conducting comprehensive and in-depth literature reviews and a theoretical synthesis of related research. Adaptive systems have been built around an individual user's characteristics, such as interests, preferences, knowledge and goals. Individual differences in the ability to use new information and communication technology have been an important issue in all fields. Performance differences in utilizing new information and communication technology are sufficiently predictable that we can begin to coordinate them. Therefore, it is necessary to understand cognitive mechanisms to explain differences between individuals as well as the levels of performance. The theoretical synthesis from this study can be applied to design intelligent (i.e., human friendly) systems in our everyday lives. Further research should explore optimization design for user, by integrating user's individual traits (such as emotion and intent) and system modules to improve the interactions of human-system in data-driven environments.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.127-136
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• 2019

User-based and item-based approaches have been developed as the solutions of the movie recommendation problem. However, the user-based approach is faced with the problem of sparsity, and the item-based approach is faced with the problem of not reflecting users' preferences. In order to solve these problems, there is a research on the combination of the two methods using the concept of similarity. In reality, it is not free from the problem of sparsity, since it has a lot of parameters to be calculated. In this study, we propose a combining method that simplifies the combination equation of prior study. This method is relatively free from the problem of sparsity, since it has less parameters to be calculated. Thus, it can get more accurate results by reflecting the users rating to calculate the parameters. It is very fast to predict new movie ratings as well. In experiments for the proposed method, the initial error is large, but the performance gets quickly stabilized after. In addition, it showed about 6% lower average error rate than the existing method using similarity.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.108-118
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• 2010

In this paper, we propose Context-based Music Recommendation (COMUS) ontology for modeling user's musical preferences and context and for supporting reasoning about the user's desired emotion and preferences. The COMUS provides an upper Music Ontology that captures concepts about the general properties of music such as title, artists and genre and also provides extensibility for adding domain-specific ontologies, such as Mood and Situation, in a hierarchical manner. The COMUS is music dedicated ontology in OWL constructed by incorporating domain specific classes for music recommendation into the Music Ontology. Using this context ontology, we believe that the use of logical reasoning by checking the consistency of context information, and reasoning over the high-level, implicit context from the low-level, explicit information. As a novelty, our ontology can express detailed and complicated relations among the music, moods and situations, enabling users to find appropriate music for the application. We present some of the experiments we performed as a case-study for music recommendation.

• Journal of Korean Library and Information Science Society
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• v.51 no.2
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• pp.245-272
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• 2020

The purpose of this study is to analyze the current status of space use in university library and draw up considerations for enhancing space utilization based on the analysis results. For this purpose, the utilization rate of each space was analyzed by categorizing the user's space of the university library, and secondly, the preferences of the major user groups were analyzed according to the characteristics of the users' space. For empirical analysis, we collected and refined users' space usage data (98,282 people's data 433,769 cases) accumulated for one year for the central library of "J" National University. Then we analyzed the usage pattern according to space and period(monthly, hourly). In addition, preferences for each major group of users (undergraduates, graduate students and graduates) in the library were analyzed through the library visitors' data (2,426,553 cases) over the same period. The results of this study can be used as preliminary research for the composition and arrangement of users' space in future university libraries.

• Korean Institute of Interior Design Journal
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• v.23 no.1
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• pp.132-142
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• 2014

The purpose of this study is to configure the spaces and elements of the roof-garden plan that users regard as important, and to investigate their preferred spaces and facilities, ultimately providing basic information for the planning of user-directed roof gardens for local residents. A questionnaire survey was used as the research method, targeting roof-garden users in the L department store of Jeollabuk-do. A total of 225 people responded to the questionnaire, and 223 of them were used for this study. The collected data was analyzed in SPSS ver.18.0, using frequency, percentage, t-test, and one way ANOVA. The respondents preferred rest space the most, followed by the convenient space, landscaping and green space, and play and exercise space. Teenage students preferred the pool and artificial spaces, such as a fountain, and planned convenient spaces and rest spaces, such as a playground. Cafes and restaurants, chairs and tables, and walking trails should be considered for single women in their twenties. Due to the high population of married couples in their thirties, the preference for play facilities and safety facilities was higher than in other groups. The well-educated population in their forties, when viewed from their preference for landscaping and green space, requires the opportunity to rest in nature. The results of this study cannot be generalized since the study was directed at roof-garden users in a department store in Jeollabuk-do. However, it is possible to provide specific information about the user-directed roof-garden plan, since the study was analyzed together with various user variables.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.457-466
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• 2004

Recommendation is to offer information which fits user's interests and tastes to provide better services and to reduce information overload. It recently draws attention upon Internet users and information providers. The collaborative filtering is one of the widely used methods for recommendation. It recommends an item to a user based on the reference users' preferences for the target item or the target user's preferences for the reference items. In this paper, we propose a neural network based collaborative filtering method. Our method builds a model by learning correlation between users or items using a multi-layer perceptron. We also investigate integration of diverse information to solve the sparsity problem and selecting the reference users or items based on similarity to improve performance. We finally demonstrate that our method outperforms the existing methods through experiments using the EachMovie data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.172-178
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• 2020

In order to recommend contents, the company generally uses collaborative filtering that takes into account both user preferences and video (item) similarities. Such services are primarily intended to facilitate user convenience by leveraging personal preferences such as user search keywords and viewing time. It will also be ranked around the keywords specified in the video. However, there is a limit to analyzing video similarities using limited keywords. In such cases, the problem becomes serious if the specified keyword does not properly reflect the item. In this paper, I would like to propose a system that identifies the characteristics of a video as it is by the system without human intervention, and analyzes and recommends similarities between videos. The proposed system analyzes similarities by taking into account all words (keywords) that have different meanings from training videos, and in such cases, the methods handled by big data clusters are applied because of the large scale of data and operations.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.113-127
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• 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 Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.825-840
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• 2015

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences(i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.111-121
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• 2009

In this paper, we propose a multimedia recommender system using user's playback time. Proposed system collects multimedia content which is requested by user and its user‘s playback time, as web log data. The system predicts playback time.based preference level and related contents from collected transaction database by fuzzy association rule mining. Proposed method has a merit which sorts recommendation list according to preference without user’s custom preference data, and prevents a false preference. As an experimental result, we confirm that proposed system discovers useful rules and applies them to recommender system from a transaction which doesn‘t include custom preferences.