• Asia pacific journal of information systems
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• v.17 no.4
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• pp.187-206
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• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.4
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• pp.19-33
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• 2012

Ministry of Land, Transport and Maritime Affairs prepared the method to update traffic connection system by amending "National Transport System Efficiency Act(hereinafter Act)". The key is a development of Intermodal Transfer Center. The law and guideline related with Intermodal Transfer Center requires the installation and operation of transfer information guide facility to improve user's convenience. However, there are no sufficient studies that can be used as references for the method to construct transfer support information system related with user's preference. The study performed the research about user's service satisfaction in relation with transfer support information service, which was embodied in model operation process, on the basis of transfer support information system of Intermodal Transfer Center applied to Gimpo Airport. The analysis result about service preference, importance of each supplied information, service satisfaction and consideration for service embodiment can be used as a guideline to embody the user information service of Intermodal Transfer Center. In addition, through CVM, the study estimated and proposed the service valuation of smart intermodal transfer service that provides customized information to cope with user's situation and traffic means operation status among transfer support information service. It is determined that the study will measure the benefit of Intermodal Transfer Center user by using monetary value when smart intermodal transfer service is supplied and provide the ground to expand high-tech transfer information service with high usefulness and convenience.

• Design Convergence Study
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• v.14 no.5
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• pp.47-56
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• 2015

People often use haptic experience as a basis for their preference decisions and value judgments, assuming that haptic experience with a product results from the properties of the products. However, research has suggested that unplanned haptic experience, which does not arise from the properties of the product itself, can also influence people's preference and value evaluation (Ackerman, Nocera, & Bargh, 2010). In this study, in order to verify (1) if such unplanned or accidental haptic experience changes user's cognitive tendency and (2) if accidental haptic experience leads to misattribution of the cause of haptic experience, two hypotheses were suggested and empirically investigated. Participants of the experiment were exposed to certain products on a display of a tablet PC and asked to decide on the maximum price they were willing to pay for each product. The products displayed on the screen were made up of either soft material or hard material. Results of the experiment revealed that accidental haptic experience had an effect on participants' value evaluation of products via altering their cognitive inclinations. Possible applicability of accidental haptic experiences that occur in various situations were discussed.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.183-192
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• 2017

This study aims to suggest improved design for both non-smokers and smokers to minimize inconvenience of smoke, at the same time, allow smoking in comfortable environment. The study was researched in three categories: First, consciousness research regarding smoking booth, second, preference research regarding product design, and third, research on emotional words about smoking booth by emotion evaluation. The result of design preference research was, first of all, smoking booth for smokers should be designed in both notable and familiar shape rather than stiff and rough shape. Second, color for the booth should apply warm colors such as white, pastel, and bright tone rather than prime colors. Third, the internal circulation filter in smoking booth should be managed thoroughly. In addition, extra seats and ventilation design is necessary to prevent passive smoking. The result of emotion evaluation was that people recognized certain words in four aspects. Each image word for factor 1 was "functional emotion', factor 2 was "psychological emotion", factor 3 as "color emotion", and factor 4 as "shape emotion". User-centered service design is necessary for both smokers and non-smokers, to minimize the damage by smoke and to spend time for short break.

• Archives of design research
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• v.12 no.3
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• pp.73-80
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• 1999

Many domestic users complain about severe pain to the waist, neck, and shoulder as well as work performance because the domestic chair design was developed with western anthropometry dimension and design criteria. Ergonomic design standards are needed for office chair design to reduce stress and poor physical posture for various user body types. In this study, we have suggested design dimensions recommended from previous studies and Korean anthropometry data. We also have reviewed users' preference dimension through measured subjects and analyzed differences between users' preference dimension and the previous design criteria to verify physical appropriateness. We evaluated general office chairs and adjustable chairs which can adjust to fit each person. we also analyzed how each design dimension was reached and affected the human body by evaluation of physical discomfort and comfort. We have found seat height is very important in a workstation. If the seat height is high, it effects the thigh. If width, height, and angle of the backrest are wrong, fatigue to the shoulder, neck, and waist, etc.. As a result of this experiment, we suggested that the height of a seat for Koreans be 425mm for the fixed type and 365-484mm for the adjustable type. Also other design recommendations were suggested in the thesis. In conclusion, our research will be very important in the database because it provides adjustable ranges to fit user's body types in the various design fields.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.93-107
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• 2009

This study investigated consumer preferences for Internet medical information service in China using the multi-attribute utility theory. The multi-attribute utility theory is a compositional approach for modeling consumer preferences wherein researchers calculate the overall service utility by summing up the evaluation results for each attribute. We found that Chinese Internet medical information users consider the availability of information and quick response to be the most important attributes. Further, they think that the comment feature is less important as compared to other attributes such as costs and updates. In addition, we found that the Internet users having more Internet experience consider these attributes to be more important as compared to the people who are just beginning to surf the Internet. For any successful Internet business, Internet marketers should assess individual-level preference and accordingly organize a fresh campaign. As of now, Internet marketers need estimation methods to predict the market performance of new services in many different business environments. We believe that the multi-attribute utility theory is a useful approach in this regard.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.619-626
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• 2012

With the service suggesting additional extended or related query, search engines aim to provide their users more convenience. The extended or related query suggestion service based on popularity, or by how many people have searched on web using the query, has limitations to elevate users' satisfaction, because each user's preference and interests differ. This paper will demonstrate the design and realization of the system that suggests extended query appropriate for users' demands, and also an improvement in the computing process between entering the first search word and the subsequent extension to the related themes. According to the evaluation the proposed system suggested 41% more extended or related query than when searching on Google, and 48% more than on Yahoo. Also by improving the shortcomings of the extended or related query system based on general popularity rather than each user's preference, the new system enhanced users' convenience further.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.63-74
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• 2005

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.61-70
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• 2004

It is difficult to find an appropriate web site because exponentially growing web contains millions of web documents. Personalization of web search can be realized by recommending proper web sites using user profile but more efficient method is needed for estimating preference because user's evaluation on web contents presents many aspects of his characteristics. As user profile has a property of non-linearity, estimation by classifier is needed and combination of classifiers is necessary to anticipate diverse properties. Structure adaptive self-organizing map (SASOM) that is suitable for Pattern classification and visualization is an enhanced model of SOM and might be useful for web mining. Fuzzy integral is a combination method using classifiers' relevance that is defined subjectively. In this paper, estimation of user profile is conducted by using ensemble of SASOM's teamed independently based on fuzzy integral and evaluated by Syskill ＆ Webert UCI benchmark data. Experimental results show that the proposed method performs better than previous naive Bayes classifier as well as voting of SASOM's.