• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4320-4330
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• 2010

Blogs that allow two way communications are open to the public more than 20 million sites these days. The study tries to categorize and classify blogs based on the common representative factors. As blogs are changing dynamically, the study also tries to figure out key factors either for growing or perishing dynamics. The study develops the research idea from the independent variables such as contents factors and psychological factors to the controlling factors such as trust, attitude, and purchasing intention. The research found out that opinion leading and information seeking tendency are significantly related to the attitude to the internet shopping mall.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.67-74
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• 2011

Collaborative recommendation is one of the most widely used methods of automated product recommendation in e-Commerce. For analyzing the customer's preference, traditional explicit ratings are less desirable than implicit ratings because it may impose an additional burden to the customers of e-commerce companies which deals with a number of products. Cardinal scales generally used for representing the preference intensity also ineffective owing to its increasing estimation errors. In this paper, we propose a new way of constructing the ordinal scale-based customer profile for collaborative recommendation. A Web usage mining technique and lexicographic consensus are employed. An experiment shows that the proposed method performs better than existing CF methodologies.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.711-719
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• 2014

Along with the advent of ubiquitous computing environment, it is becoming a part of our common life style that the demands for enjoying the wireless internet using intelligent portable device such as smart phone and iPad, are increasing anytime or anyplace without any restriction of time and place. The recommending service becomes a very important technology which can find exact information to present users, then is easy for customers to reduce their searching effort to find out the items with high purchasability in e-commerce. Traditional mining association rule ignores the difference among the transactions. In order to do that, it is considered the importance of type of merchandise or service and then, we suggest a new recommending service using mining sequential pattern based on weight to reflect frequently changing trends of purchase pattern as time goes by and as often as customers need different merchandises on e-commerce being extremely diverse. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

• The KIPS Transactions:PartC
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• v.9C no.2
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• pp.181-188
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• 2002

There are recently a lot of inconvenience because every client should be set to the proxy server on the browser in order to control the access by means of the client authentication in the proxy server. The client authentication technology using the transparency cache in this paper will be transparently used for every user in the internet which option of the authentication function is simply set in the cache server. In addition, the administrator will get the benefit since he can control the traffic of each client and strengthen the security. And also, this system is expected to use in the eCRM deeply rotated to the tendency of the client in the field of the e-commerce like shopping mall in the internet since the administrator can monitor the pattern of the client using the internet. This technique can be applied to the company affiliated research center, the EC website, and the military where it is essential for the tight security even though there are no additional security devices.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.59-68
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• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.129-141
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• 2010

An Internet shopping mall for clothing operates a warehouse for packing and shipping products to fulfill its orders. All the products in the warehouse are put into the boxes of same brands and the boxes are stored in a row on shelves equiped in the warehouse. To make picking and managing easy, boxes of the same brands are located side by side on the shelves. When new products arrive to the warehouse for storage, the products of a brand are put into boxes and those boxes are located adjacent to the boxes of the same brand. If there is not enough space for the new coming boxes, however, some boxes of other brands should be moved away and then the new coming boxes are located adjacent in the resultant vacant spaces. We want to minimize the movement of the existing boxes of other brands to another places on the shelves during the warehousing of new coming boxes, while all the boxes of the same brand are kept side by side on the shelves. Firstly, we define the adjacency of boxes by looking the shelves as an one dimensional series of spaces to store boxes, i.e. cells, tagging the series of cells by a series of numbers starting from one, and considering any two boxes stored in the cells to be adjacent to each other if their cell numbers are continuous from one number to the other number. After that, we tried to formulate the problem into an integer programming model to obtain an optimal solution. An integer programming formulation and Branch-and-Bound technique for this problem may not be tractable because it would take too long time to solve the problem considering the number of the cells or boxes in the warehouse and the computing power of the Internet shopping mall. As an alternative approach, we designed a fast heuristic method for this reallocation problem by focusing on just the unused spaces-empty cells-on the shelves, which results in an assignment problem model. In this approach, the new coming boxes are assigned to each empty cells and then those boxes are reorganized so that the boxes of a brand are adjacent to each other. The objective of this new approach is to minimize the movement of the boxes during the reorganization process while keeping the boxes of a brand adjacent to each other. The approach, however, does not ensure the optimality of the solution in terms of the original problem, that is, the problem to minimize the movement of existing boxes while keeping boxes of the same brands adjacent to each other. Even though this heuristic method may produce a suboptimal solution, we could obtain a satisfactory solution within a satisfactory time, which are acceptable by real world experts. In order to justify the quality of the solution by the heuristic approach, we generate 100 problems randomly, in which the number of cells spans from 2,000 to 4,000, solve the problems by both of our heuristic approach and the original integer programming approach using a commercial optimization software package, and then compare the heuristic solutions with their corresponding optimal solutions in terms of solution time and the number of movement of boxes. We also implement our heuristic approach into a storage location assignment system for the Internet shopping mall.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Global Scholars of Marketing Science
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• v.14
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• pp.17-37
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• 2004

This research is speak that is tendencious in comprehension of external consumer information search behavior using on-line external information source to consumers who use on-line that is used as corporations' main strategic means. That is, classify consumer groups which was atomized according to type inflict consumer's information search knowledge level and shopping value study which use on-line, and decision factors of information search that these groups can influence a difference or each group which use information sources grasped what it is. Result that investigate information search knowledge level difference about study finding on-line information source utilization used mainly portal site, comparison site, auction site. And, utilization shopping pursuit value group used information source by portal site, auction site, niche shopping mall site and hedonic shopping pursuit value group used information source by portal site, auction site, shopping mall site. It confirmed that all variables(i.e. consumer-based variable and web site-based variable) are influencing variously in on-line external information search types. Finally, we proposed different way to erect strategic model about consumers that use on-line with study finding that see.

• The Journal of Information Systems
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• v.15 no.3
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• pp.131-157
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• 2006

A lack of trust in the technical and institutional environments surrounding the web can hinder e-commerce adoption, because Internet social cues are minimal and trust is difficult th establish. Web vendors must act purposefully to overcome consumer perceptions of uncertainty and risk by building trust-both in their own web sites and in the broader Internet environments. Trust makes consumers comfortable by sharing personal information making purchases, and acting on web vendor advice-behaviors essential to wide-spread adoption of e-commerce. Understanding the nature and antecedents of trust is, therefore, a major issue for both Internet researchers and practitioners. Prior research on e-commerce trust has used diverse, incomplete, and inconsistent definitions of trust therefore, making it difficult to compare results across studies. This study tried to and the empirical relationships among the trust-related factors on the usage of Internet shooing mall. The model includes five high-level constructs-disposition to trust institution-based trust trusting beliefs, trusting intention and environmental factors. The results showed that the disposition to trust and the environmental factors had a significant effect on the web site trust however, their effect was not consistent Moreover, the model suggested in this study need to be extended including with the more sociological factors and results in this study required to be verified with those extended model.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.185-203
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• 2009

The literature reveals that service quality is critical determinants affecting customer satisfaction in the retail industry. Recent online B2C studies also show that service quality significantly influences online customer satisfaction. However, research on analyzing antecedents to determine the service quality level of online shopping has not been sufficiently reported. Moreover, the offline synergy effect on the Internet shopping mall has been presented mostly at a conceptual level. The purpose of this study is to investigate key managerial variables exploiting offline synergy, including pure online attributes, that impact on service quality of the click and mortar Internet shopping mall. A survey was conducted for empirical validation of the hypotheses. The results indicate that core offline synergy factors significantly enhance the perceived service quality level of the Internet shopping mall. Implications, limitations, and future research directions are also discussed.