At this time, the two main techniques for achieving web personalization assistant systems generally concern direct manipulation and software agents. While both direct manipulation and software agents are intended for permitting user to complete tasks rapidly, efficiently, and easily, their methodologies are different. The central debate involving these web personalization techniques originates from the amount of control that each allows to, or holds back from, the users. Direct manipulation can provide users with comprehensibel, predictable and controllable user interfaces that give them a feeling of accomplishnent and responsibility. On the other hand, the intelligent software components, the agents, can assist users with artificial intelligence by monitoring or retrieving personal histories or behaviors. In this empirical study, two web personalization assistant systems are evaluated. One of them, WebPersonalizer, is an agent based user personalization tool; the other, AntWorld, is a collaborative recommendation tool which provides direct manipulation interfaces. Through this empirical study, we have focused on two different paradigms as web personalization assistant systems : direct manipulation and software agents. Each approach has its own advantages and disadvantages. We also provide the experimental result that is worth referring for developers of electronic commerce system and suggest the methodologies for conveniently retrieving necessary information based on their personal needs.
Recently, distributed collaborative development environment has been recognized an alternative environment for product development in which multidisciplinary participants are naturally involving. Reuse of Product design information has long been recognized as one of core requirements for efficient product development. This paper addresses an image-based retrieval system to support product design information reuse. In the system, product images obtained from multi-modal devices are utilized to reuse design information. The proposed system conducts the segmentation of a product image by using a labeling method and generates an attributed relational graph (ARG) that represents properties of segmented regions and their relationships. The generated ARG is extended by integrating corresponding part/assembly information. In this manner, the reuse of assembly design information using a product image has been realized. The main advantages of the presented system are following. First, the system is not dependent to specific design tools, because it utilizes multimedia images that can be obtained easily from peripheral devices. Second ratio-based features extracted from images enable image retrievals that contain various sizes of parts. Third, the system has shown outstanding search performance, because we applied various information of segmented part regions and their relationships between parts.
Purpose - Over 90% of Domestic logistics industry is small enterprise and they are experiencing growth stagnation due to price-based competition structure rather than constructing logistics service of high added value. In order to get over this situation and pursue the development of logistics industry, strengthening its competitiveness, through inter-enterprise cooperative network build-up, would be a key alternative. Therefore, in this study, an index for measuring inter-enterprise cooperation level of Joint logistics business will be developed as a typical collaborative business model in logistics industry. Moreover, a strengthening competitiveness method suggests a developmental step and a key management index to mature in logistics industry. Research Design, Data, Methodology - This study is an index development research for measuring inter-enterprise cooperation level of logistics industry. Such a level was measured by performing a survey by targeting enterprises that participated in Joint logistics business. The targeting enterprises are typical cooperative models in logistics industry. Measurement items were developed which were based on the presented items in existing research. Question items were composed of selection type questions as answering Yes/No. They measures implementation status of corporate activity and detailed activity items measuring qualitative level. Total samples were based on 116 enterprise samples including 90 logistics enterprises and 26 shippers. In addition, by evaluating the importance for Joint logistics business recognition with personnel working level, the weight of measuring variable was extracted. This study has built an assessment tools (LPCI) on Joint logistics business cooperation level in a situation where there are no previous studies on joint logistics business, this study is meaningful for other studies. Results - As a result of analyzing LPCI presented in this study, the score of logistics enterprise was represented as 59.9 points based on full score of 100 points and that of shippers as 47.2 points and cooperation level among enterprises participated in Joint logistics business was revealed to be very low. In particular, as a result of measuring the importance between logistics enterprise and shippers, the difference by each measurement standard was represented among those enterprises. This difference is considered to be a key factor that cooperative operational conformity between logistics enterprises and shippers is represented to be low. Conclusions - As most joint logistics business, being promoted at present, is sharing facility and information with joint logistics business, it is hard to find such a joint logistics business in reality based on cooperative business model in main cooperation agents. Therefore, competitiveness of logistics industry could be strengthened by promoting joint logistics business based on their mutual cooperation among enterprises. In other words, it is to secure sustainable competitiveness of joint logistics business together with creation of new market by inter-enterprise cooperation based on integration of basic logistics business.
It is important that acquire information about if customer has some habit in electronic commerce application of internet base that led in recommendation service for customer in dynamic web contents supply. Collaborative filtering that has been used as a standard approach to Web personalization can not get rapidly user's preference change due to static user profiles and has shortcomings such as reliance on user ratings, lack of scalability, and poor performance in the high-dimensional data. In order to overcome this drawbacks, Web usage mining has been prevalent. Web usage mining is a technique that discovers patterns from We usage data logged to server. Specially. a technique that discovers Web usage patterns and clusters patterns is used. However, the discovery of patterns using Afriori algorithm creates many useless patterns. In this paper, the enhanced method for the construction of dynamic user profiles using validated Web usage patterns is proposed. First, to discover patterns Apriori is used and in order to create clusters for user profiles, ARHP algorithm is chosen. Before creating clusters using discovered patterns, validation that removes useless patterns by Dempster-Shafer theory is performed. And user profiles are created dynamically based on current user sessions for Web personalization.
Journal of the Korea Society of Computer and Information
/
v.19
no.2
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pp.193-200
/
2014
Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user's features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user's features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.
The purpose of this study was to assess musically gifted students'needs in order to develop the gifted curriculum in music. The survey was carried out with 103 musically gifted students who are being educated in institutes for the gifted. The survey asked the needs about components of the gifted curriculum in music: the educational objectives, contents, teaching strategies, evaluation as well as educational environment influencing on the curriculum. As for the objectives, the result showed the highest needs was the ability to communicate with audience by expressing one's feeling. For the high school students, a large number items had significant differences between the necessary level and the current level. As for the contents, the highest needs were the class piano, second instrument, and the experience of the musical field. High school students needed the second instrument more than middle school students did. As for the teaching strategies, the highest needs were the autonomous choice by learners, the instruction pursuing learners' interests, and the field work. As for the evaluation, the highest needs were the peer evaluation and the evaluation on the collaborative performance or team work. As for the educational environment, the gifted in music strongly needed spaces to practice instruments. Additionally, high school students needed a space to perform like a concert hall. Thus the gifted curriculum in music must be thoroughly developed based on the result above.
This study aimed to investigate students' learning process by examining their perception process of problem structure and mathematization, and further to suggest an effective teaching and learning of mathematics to improve students' problem-solving ability. Using the qualitative research method, the researcher observed the collaborative learning of two middle school students by providing problem-posing activities of five lessons and interviewed the students during their performance. The results indicated the student with a high achievement tended to make a similar problem and a new problem where a problem structure should be found first, had a flexible approach in changing its variability of the problem because he had advanced algebraic thinking of quantitative reasoning and reversibility in dealing with making a formula, which related to developing creativity. In conclusion, it was observed that the process of problem posing required accurate understanding of problem structures, providing students an opportunity to understand elements and principles of the problem to find the relation of the problem. Teachers may use a strategy of simplifying external structure of the problem and analyzing algebraical thinking necessary to internal structure according to students' level so that students are able to recognize the problem.
As personal devices and pervasive technologies for interacting with networked objects continue to proliferate, there is an unprecedented world of scattered pieces of contextualized information available. However, the explosive growth and variety of information ironically lead users and service providers to make poor decision. In this situation, recommender systems may be a valuable alternative for dealing with these information overload. But they failed to utilize various types of contextual information. In this study, we suggest a methodology for context-aware recommender systems based on the concept of contextual boundary. First, as we suggest contextual boundary-based profiling which reflects contextual data with proper interpretation and structure, we attempt to solve complexity problem in context-aware recommender systems. Second, in neighbor formation with contextual information, our methodology can be expected to solve sparsity and cold-start problem in traditional recommender systems. Finally, we suggest a methodology about context support score-based recommendation generation. Consequently, our methodology can be first step for expanding application of researches on recommender systems. Moreover, as we suggest a flexible model with consideration of new technological development, it will show high performance regardless of their domains. Therefore, we expect that marketers or service providers can easily adopt according to their technical support.
Today, many services and products that used to be only provided on offline have been being provided on the web according to the improvement of computing environment and the activation of web usage. These web-based services and products tend to be provided to customer by customer's preferences. This paradigm that considers customer's opinions and features in selecting is called personalization. The related research field is a recommendation. And this recommendation is performed by recommender system. Generally the recommendation is made from the preferences and tastes of customers. And recommender system provides this recommendation to user. However, the recommendation techniques have a couple of problems; they do not provide suitable recommendation to new users and also are limited to computing space that they generate recommendations which is dependent on ratings of products by users. Those problems has gathered some continuous interest from the recommendation field. In the case of new users, so similar users can't be classified because in the case of new users there is no rating created by new users. The problem of the limitation of the recommendation space is not easy to access because it is related to moneywise that the cost will be increasing rapidly when there is an addition to the dimension of recommendation. Therefore, I propose the solution of the recommendation problem of new user and the usage of item quality as weight to improve the accuracy of recommendation in this paper.
Journal of the Korea Society of Computer and Information
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v.25
no.6
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pp.109-117
/
2020
An edutainment system aims to help learners to recognize problems effectively, grasp and classify important information needed to solve the problems and convey the contents of what they have learned. Edutainment contents can be usefully applied to education and training in the both scientific and industrial areas. Our present work proposes an edutainment system that can be applied to a drug discovery process including virtual screening by using intuitive multi-modal interfaces. In this system, a stereoscopic monitor is used to make three-dimensional (3D) macro-molecular images, with supporting multi-modal interfaces to manipulate 3D models of molecular structures effectively. In this paper, our system can easily solve a docking simulation function, which is one of important virtual drug screening methods, by applying gaming factors. The level-up concept is implemented to realize a bio-game approach, in which the gaming factor depends on number of objects and users. The quality of the proposed system is evaluated with performance comparison in terms of a finishing time of a drug docking process to screen new inhibitors against target proteins of human immunodeficiency virus (HIV) in an e-drug discovery process.
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