• The Journal of Society for e-Business Studies
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• v.7 no.2
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• pp.55-68
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• 2002

Internet group buying systems have been widely used recently. In those systems, because the reserve price is provided by the buyer, the success rate can be decreased if the reserve price is set too low compared with the normal price. Otherwise, an unsuitable successful bid can be made if the reserve price is set too high based on inaccurate information. Likewise, the seller's providing too high a bid price can deteriorate his/her own successful bid rate, whereas a successful bid with too low a price may make no profit in the sale. Therefore, pricing agents that recommend adequate prices based on the past buying and selling history data can be helpful. In this paper, we propose two kinds of agents. One suggests reserve prices to buyers based on the past buying history database of the system. The other recommends bid prices to a seller based on the past bidding history data of the company using the cost accounting theory. Through performance experiments, we show that the successful bid rate can increase by preventing buyers from making unreasonable reserve prices. Also, we show that, for the seller, the rate of successful bids with appropriate profits can increase. Using the pricing agents, we design and implement an XML-based group buying system.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.193-200
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• 2022

In this study, the intellectual property rights of digital contents (creations) are protected by using block chain technology that cannot be damaged or forged. So, we build a blockchain-based content sales revenue tracking system and platform that activates the transaction and distribution of digital content (creation). We developed an API server that can be used for content registration and revision history management smart contract, license management smart contract according to content purchase, content inquiry function through files and hashes, and web and APP services. Through this, it is possible to prove the relationship between the rights of the creators of digital content creations and protect the rights of the creators.

• Journal of IKEEE
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• v.22 no.1
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• pp.156-161
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• 2018

Recently, as the population rapidly increases, food shortages and waste are emerging serious problem. In order to solve this problem, various countries and enterprises are trying research and product development such as a study of consumers' purchasing patterns of food and a development of smart refrigerator using IoT technology. However, the smart refrigerators which currently sold have high price issue and another waste due to malfunction and breakage by complicated configurations. In this paper, we proposed a low-cost smart refrigerator system based on IoT for solving the problem and efficient management of ingredients. The system recognizes and registers ingredients through QR code, image recognition, and speech recognition, and can provide various services of the smart refrigerator. In order to improve an accuracy of image recognition, we used a model using a deep learning algorithm and proved that it is possible to register ingredients accurately.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.803-813
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• 2013

The purchase of items in e-commerce is a little bit different from that of items in off-line. The recommendation of items in off-line is conducted by salespersons' recommendation, However, the item recommendation in e-commerce cannot be recommended by salespersons, and so different types of methods can be recommended in e-commerce. Recommender system is a method which recommends items in e-commerce. Preferences of customers who want to purchase new items can be predicted by the preferences of customers purchasing existing items. In the recommender system, the items with estimated high preferences can be recommended to customers. The algorithm of collaborative filtering is used in recommender system of e-commerce, and the list of recommended items is made by estimated values, and then the list is recommended to customers. The dataset used in this research are 100k dataset and 1 million dataset in Movielens dataset. Similar results in two dataset are deducted for generalization. To suggest a new algorithm, distribution features of estimated values are analyzed by the existing algorithm and transformed algorithm. In addition, respondent'distribution features are analyzed respectively. To improve the collaborative filtering algorithm in neighborhood recommender system, a new algorithm method is suggested on the basis of existing algorithm and transformed algorithm.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used