• Proceedings of the Korean Society of Computer Information Conference
• /
• 2012.07a
• /
• pp.135-137
• /
• 2012

A existing recommedation system using association rules has the problem, which is suffered from inefficiency by reprocessing of the data which have already been processed in the incremental data environment in which new data are added persistently. We propose the recommendation technique using incremental frequent pattern mining based on RFM in e-commerce. The proposed can extract frequent items and create association rules using frequent patterns mining rapidly when new data are added persistently.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.3
• /
• pp.435-439
• /
• 2022

Many of the industry was changed because of the pandemic of covid 19. It combined with the tendency of modern people to pursue convenience. The industry of Cosmetics also changed business channel from offline to online. Before, people can not get suggestions after they complete the survey. This paper research how to suggest some cosmetics products with their skin type and skin data. We will develop Beauty Concierge system that can get suggestion after the survey. It's will make people attend activity and can make more benefit to the people.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.3
• /
• pp.691-699
• /
• 2011

Recently, diverse approaches retrieval services based on social network are suggested. Although existing recommendation systems can retrieve experts of specific fields, profiles and evaluations about experts that users want to be recommended are in a system. The proposed expert recommendation system can automatize collection of evaluation to evaluate experts and experts' profiles in separate systems by using the Knowledge Base and XMDR. We also attempt to construct system which can recommend a number of experts by dynamically constructing Social Network by using diverse resources distributed 로컬ly and composed of heterogeneous data sources. To resolve these problems efficiently, there is a need to provide constructed resources between heterogeneous systems with transparency and independence and provide users with a singular interface. Therefore, the proposed system in this paper uses Knowledge Base and XMDR for extracting distributed experts' profiles and designs expert recommendation system connecting Knowledge Base with Social Network.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.2
• /
• pp.197-206
• /
• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.6
• /
• pp.163-172
• /
• 2012

Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.