Song Chul Moon

Clustering Method Using Item Preference Based on RFM for Recommendation System in U-Commerce

This paper proposes a new method using clustering of item preference based on Recency, Frequency, Monetary (RFM) for recommendation system in u-commerce under fixed mobile convergence service environment which is required by real time accessibility and agility. In this paper, using an implicit method without onerous question and answer to the users, not used user’s profile for rating to reduce customers’ search effort, it is necessary for us to keep the scoring of RFM to be able to reflect the attributes of the item and clustering in order to improve the accuracy of recommendation with high purchasability. 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.

Mining Association Rules Using RFM Scoring Method for Personalized u-Commerce Recommendation System in Emerging Data

This paper proposes a new mining technique using RFM (Recency, Frequency, Monetary) scoring method for personalized u-commerce recommendation system in emerging data under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, using a implicit method without onerous question and answer to the users to reduce customers’ search effort, it is necessary for us to keep the analysis of RFM scoring method to reflect the attributes of the item and to generate association rules based on the most frequently purchased data extracted from the whole data with the item RFM score to recommend the items with high purchasability according to the threshold for creative association rules with support, confidence and lift. To verify improved performance, we make experiments with dataset collected in a cosmetic internet shopping mall.

Effective Purchase Pattern Mining with Weight Based on FRAT Analysis for Recommender in e-Commerce

This paper proposes a new recommending method using effective purchase pattern mining with weight based on FRAT (Frequency, Regency, Amount and Type of merchandise or service) analysis in e-commerce. In this paper, using an implicit method without onerous question and answer to the users, it is necessary for us to make the task of mining frequent pattern in purchase data extracted the most frequently from whole data, to join customer’s data, to keep the analysis of FRAT to calculate the weigh and to make the task of clustering of item category in order to recommend item with an immediate effect by frequently changing trends of purchase pattern. We consider the importance of type of merchandise or service and then, suggest recommending method using mining frequent pattern with weight based on FRAT analysis to forecast frequently changing trends by emphasizing the important items with efficiency and to reflect different merchandises on e-commerce being extremely diverse for customers’ need. 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.

Personalized u-commerce recommending service using weighted sequential pattern with time-series and FRAT method

This paper proposes a new personalized u-commerce recommending service using weighted sequential pattern with time-series and FRAT(Frequency, Regency, Amount and Type of merchandise or service) method under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, using an implicit method without onerous question and answer to the users, it is necessary for us to make the FRAT score and the task of mining sequential pattern with time-series in order to do recommending service based on periodicity analysis by timely changing trends of seasonable pattern, and to improve the accuracy of recommendation with high purchasability To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

Clustering Method Using Weighted Preference Based on RFM Score for Personalized Recommendation System in u-Commerce

This paper proposes a new clustering method using the weighted preference based on RFM(Recency, Frequency, Monetary) Score for personalized recommendation in u-commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, using an implicit method without onerous question and answer to the users, not used user’s profile for rating, it is necessary for us to extract the most frequent purchase items from the whole purchase data and to calculate the weighted preference of item for customer in order to reduce customers’ search effort, to reflect frequently changing trends by emphasizing the important items and to improve the rate of recommendation with high purchasability. 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.

Weighted Mining Association Rules Based Quantity Item with RFM Score for Personalized u-Commerce Recommendation System

This paper proposes a new weighted mining technique based quantity item with RFM(Recency, Frequency, Monetary) score for personalized u-commerce recommendation system under ubiquitous computing, or pervasive computing environment. Traditional association rule mining ignores the difference among the transactions. In this paper, it is necessary for us to consider the quantity of purchased data by each rank of RFM score in order to have different weights for different transactions, to generate weighted association rules through weighted mining association rules based quantity item with RFM score, and to recommend the items with high purchasability according to the threshold for creative weighted association rules with w-support, w-confidence and w-lift. To verify improved performance, we make experiments with dataset collected in a cosmetic internet shopping mall.

Efficient Purchase Pattern Clustering Based on SOM for Recommender System in u-Commerce

This paper proposes an efficient purchase pattern clustering method based on SOM(Self-Organizing Map) for Personal Ontology Recommender System in u-Commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, it is necessary for us to keep clustering the user’s information to join the user’s score based on RFM factors using SOM network and the analysis of RFM to be able to reflect the attributes of the user in order to reflect frequently changing trends of purchase pattern by emphasizing the important users and items, and to improve better performance of recommendation. The proposed makes the task of an efficient purchase pattern clustering based on SOM for preprocessing so as to be possible to recommend by the loyalty of RFM factors as considering user’s propensity. 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.

Weighted Mining Frequent Itemsets Using FP-Tree Based on RFM for Personalized u-Commerce Recommendation System

This paper proposes a new weighted mining frequent itemsets using FP-tree based on RFM for personalized u-commerce recommendation system under ubiquitous computing. Existing recommendation system using association rules still does not only reflect exact attributes of item but also has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy. In this paper, to solve these problems, it is necessary for us to make RFM(Recency, Frequency, Monetary) score of item and to extract the most frequently purchased data from the whole data in order to improve the accuracy of recommendation, to consider frequently changing the weighted patterns by emphasizing the important items with high purchasability according to the threshold for creative the weighted mining frequent itemsets using FP-tree without occurrence of candidate set. To verify improved performance, we make experiments with dataset collected in a cosmetic internet shopping mall.

Bayesian Learning through Weight of Listener’s Prefered Music Site for Music Recommender System

Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because it is convenient and affordable for the listeners to do that. We use Bayesian learning through weight of listener’s prefered music site such as Melon, Billboard, Bugs Music, Soribada, and Gini. We reflect most popular current songs across all genres and styles for music recommender system using user profile. It is necessary for us to make the task of preprocessing of clustering the preference with weight of listener’s preferred music site with popular music charts. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.

Learning Listener's Preference for Music Recommender System

Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the listeners. In this paper, we propose music recommender system using learning listeners prefererece, such as Melon, Billboard, Bugs Music, Soribada, and Gini, with most popular current songs across all genres and styles. It is also necessary for us to make the task of calculating the preference with weight to reflect the preference of most popular current songs with its popular music charts on trends. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.