• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.7
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• pp.925-934
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• 2017

R-tree is index structure which is frequently used for handling spatial data. However, if the number of dimensions increases, or if only partial dimensions are used for searching the certain data according to user preference, the time for indexing is greatly increased and the efficiency of the generated R-tree is greatly reduced. Hence, it is not suitable for the multidimensional data, where dimensions are continuously increasing. In this paper, we propose a multidimensional hash index, a new multidimensional index structure based on a hash index. The multidimensional hash index classifies data into buckets of euclidean space through a hash function, and then, when an actual search is requested, generates a hash search tree for effective searching. The generated hash search tree is able to handle user preferences in selected dimensional space. Experimental results show that the proposed method has better indexing performance than R-tree, while maintaining the similar search performance.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.71-80
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• 2006

As e-commerce developing rapidly, it is becoming a research focus about how to find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. One of the most successful and widely used technologies for building personalization and goods recommendation system is collaborating filtering. However, collaborative filtering have serious data sparsity problem. Traditional association rule does not consider user's interests or preferences to provide a user with specific personalized service.In this paper, we propose an goods recommendation system, which is integrated an collaborative filtering algorithm with item-to-item corelation and an improved Apriori algorithm. This system has user's interests or preferences ro provide a user with specific personalized service.

• Journal of KIISE:Software and Applications
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• v.33 no.3
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• pp.356-364
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• 2006

'Ubiquitous Computing' is the most important paradigm of the next generation Information-Communication technology. The one of important problems to develop ubiquitous computing service system get hold of relations between times of transfer objects and events of transfer objects. Another problem is what reason transfer-pattern through location data of transfer objects. In this paper, we propose an approach to offer temporal-relation service in ubiquitous computing environment. The first is temporal reasoning in service viewpoint. The second is temporal reasoning to record user's preference. Users have preferences that are closely connected with time. These preferences are recorded at user profile. Therefore, the user profile-based ubiquitous service system can offer suitable service to users.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.346-350
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• 2009

According to the development of information technology, there are many services in our life. These services make our life safe and convenient. However, the increment of the services also causes the increment of human concern and effort to control these services. The context-aware service is the service that provided their functionality at the right time and to the right place by analysis user's current situation. The most previous studies about context-aware service regard that context-aware services are defined by the developer who has expertise in information technology. The definition of the context-aware services by the developer makes difficult to reflect user's personal preferences and life pattern to the services. In this paper, we propose an user interface EUCAS(by End-User, Context-Aware Service development) that make the user can define and manage their own context-aware service according to their preferences. We expect EUCAS can be effective user interface technology for providing personalized context-aware service.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.76-82
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• 2011

Most previous work on network selection in heterogeneous wireless networks has concentrated on the quality of the network alone. Therefore, users are not satisfied with network quality based network selection due to different user preferences. To solve this problem, we proposes a QoE-aware network selection algorithm that is based on the consumption patterns of user preferences which is divided into normal user, cost-sensitive user, quality-sensitive user and video quality as well as network quality. As a result of experiments, cost-sensitive user and quality-sensitive user are satisfied with enhanced QoE by 36% and 3% from the proposed network selection algorithm compared to the normal user, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.343-349
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• 2009

In this paper, it would be shown that individuals can have different responses to the same email based on their preferences through computing the distributions of user clusters' email responses from clustering results based on email users' preference information. This paper presents an approach that incorporates user preferences to construct an anti-spam mail system, which is different from the conventional content-based ones. We consider email category information derived from the email content as well as user preference information. We also build a user preference ontology to formally represent the important concepts and rules derived from a data mining process and then apply a rule optimization procedure to exclude unnecessary rules. Experimental results show that our user preference based system achieves good performance in terms of accuracy, the rules derived from the system and human comprehensibility.

• Journal of Digital Convergence
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• v.5 no.1
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• pp.69-76
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• 2007

This paper presents a study on the introduction of User Model-Based Dialogue System. Also we present a plan-based Korean dialogue system as a natural language database user interface for product search. The system can be characterized by its support for mixed initiative to give user more control over dialogue, employment of user model to reflect user's preferences, alternative solution suggestion if there is no product matched exactly to user's requirements, handling circumlocution which frequently occurs in dialogues. The user modeling shell system BGP-MS is adapted for the system. The system provides for a user-friendly database user interface by managing dialogue intelligently. By its implementation and test, it has been shown that the user model-based dialogue system can be utilized effectively for product search.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1068-1082
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• 2015

Due to the rapid advancement of the mobile technology, smart phones have been widely used in the current society. This lead to an easier way to retrieve video contents using web and mobile services. However, it is not a trivial problem to retrieve particular video contents based on users' specific preferences. The current movie recommendation system is based on the users' preference information. However, this system does not consider any emotional means or perspectives in each movie, which results in the dissatisfaction of user's emotional requirements. In order to address users' preferences and emotional requirements, this research proposes a movie recommendation technology to represent a movie's emotion and its associations. The proposed approach contains the development of emotion ontology by representing the relationship between the emotion and the concepts which cause emotional effects. Based on the current movie metadata ontology, this research also developed movie-emotion ontology based on the representation of the metadata related to the emotion. The proposed movie recommendation method recommends the movie by using movie-emotion ontology based on the emotion knowledge. Using this proposed approach, the user will be able to get the list of movies based on their preferences and emotional requirements.

• Smart Media Journal
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• v.9 no.4
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• pp.73-80
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• 2020

With the advent of the Internet era, consumer lifestyles have been changed tremendously, and mobile payment has carried out an increasingly extensive coverage of the people's life trajectory. Taking the design of the mobile payment interface as an example, we use a conjoint analysis method to survey college students in Guangxi, where questionnaires are collected from 270 people in different groups according to gender. The method separates the attributes that affect consumer choice of mobile payment interface design and the utility value of the attribute level to analyze consumer needs and preferences, and then obtains consumers' potential evaluation criteria for mobile payment interface design. The results of the study show that the attributes that influence consumers' choice of mobile payment interface design are, in order of preference: page layout, identification convenience, verification, module distribution, entertainment, and information encryption. Consumer groups of different genders show differences in their preferences in the mobile payment interface design and Consumer needs reflect consumer psychology. Several findings are obtained on the consumers' preferences on the mobile payment interface design, which may be used to improve future design processes.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.