• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• (The)Study of the Eastern Classic
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• no.72
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• pp.157-183
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• 2018

The purpose of this study was to investigate the existence aspects of the Hyangri class in Imsilhyeon, Jeolla Province in the latter half of Joseon based on Woonsuyeonbangseonsaengan, which provides lists of Hyangris in Imsilhyeon from the fourth year(1724) of King Gyeongjong's reign to the early 20th century. It contained the names of total 704 Hyangris, who included 119 Kims, 103 Eoms, 103 Jins, 87 Parks, 86 Muns, 66 Lees, 31 Baeks, 27 Hwangs, and 17 Taes. In addition, there were 12 more family names that produced fewer than ten Hyangris. Based on the share of representative family names among the Hyangris of the area, it is estimated that the dominant family names were Kim, Eom, Jin, Park, Mun, and Lee. Another interesting aspect is that the Jeon and Yang families produced no Hyangris in the 19th century, whereas the Hwang family produced 5% of Hyangris in the century with the Jin family accounting for 10% or more. These findings show that little changes were consistent within the community of Hyangris despite the fact that a couple of families were dominant. The family clans of the family names were checked in Nosogyean, which records that they were the Kim family of Gyeongju, Eom family of Yeongwol, Jin family of Namwon, Park family of Hamyang, Mun family of Nampyeong, and Lee family of Gyeongju. The study then examined the family names of 76 Hojangs that were recorded to hold the Hojang title in Woonsuyeonbangseonsaengan to see whether the family names that produced higher-level Hyangris were the same as the ones above. There was an overall agreement between the family names that produced a lot of Hojangs and those that produced the most Hyangris, but there were differences according to the periods. Six family names produced Hojangs in similar percentage in the 18th century, and only three family names, which were the Jin family of Namwon(13), Mun family of Nampyeong(9), and Eom family of Yeongwol(6), produced more than ten Hojangs in the 19th century. Other noteworthy changes in the 19th century include the rapidly rising frequency of Hojangs serving the term twice or more compared with the 18th century and the concentration of Hojangs on certain family names. These findings indicate that six family names coexisted in the active production of Hyangris in the community of Hyangris in Imsilhyeon in the latter half of Joseon, that there were changes to the family names of higher-level Hyangris internally according to the periods, and that a shift happened toward the leadership of certain family names in the society of Hyangris.