Product Recommender Systems using Multi-Model Ensemble Techniques (다중모형조합기법을 이용한 상품추천시스템)
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- 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
This study was executed to analyze the user's impact on vegetation structure changes and user's psychology in Odongdo Island of Hallyo-Haesang National Park. Five sites were sampled for vegetation structure changes vi the study area according to the extent of impact observed. Also user's psychology was studied through questionnaire with the visitors and 366 answers were collected at random sampling in May, 1986. Evergreen broad-leaved forest, i.e. Machilus thunbergii, Cinnamomum camphora, and Camellia japonica forest, took possession of 32.5% (3.91ha) of total forest area when condisering the actual vegetation. Camellia japonica community covered 40.0% (4.72ha) and Sasa coreana community took possession of 41.8% (5.02ha). The area of environmental impact grade 3 and 4 area covered 44.3% of total forest area and it should be restored because self-repair seemed to be impossible. The evergreen broad-leaved forest was destoryed seriously with no younger trees in middle and lower layers by overuse impact and would be bared soon. So the preservation of autochthonous flora is required by the control of the number of users. It was shown that most of visitors come on holidays and Sunday and places which were favorably impressed were shown as the area of showing the sea and Camellia forest. Overall levels of satisfaction was comparatively low, consequently 55% of visitors were satisfied. This level of satisfaction was associated with number of users, landscape of forest and number of facilities.
This study was conducted with the goal of analyzing the needs of Truckers, who are the main users of the cargo transportation platform, to find a development direction for the improvement and activation of the cargo transportation platform service. For this purpose, this study conducted an empirical analysis through IPA analysis targeting Truckers using domestic cargo transportation platforms. As a result of the IPA analysis, in the case of importance, fair fare presentation, unfair trade prevention measures, transportation payment deadline accuracy, transportation section information provision accuracy, quick interaction, application system quality, ease of use, user-customized service, settlement service support function, Variety of transportation sections, brand image, and additional services were shown in order. In the case of satisfaction, ease of use, settlement service support function, transport section information provision accuracy, application system quality, brand image, additional services, user-customized service, transport section diversity, quick interaction, transport payment deadline accuracy, unfair transaction prevention Measures and fair fares were presented in that order. The analysis results show high satisfaction with the ease of use related to the platform quality and the function to support the settlement work. These results can be seen as the result of a significant investment in system quality improvement for the convenience of users by suppliers in the domestic cargo transportation platform industry, where competition has become fierce. As the factors most urgently needed for improvement, 'observance of payment deadlines', 'prevention of unfair trade', and 'provision of fair fares' were selected, and these are related to chronic problems in the domestic freight transportation industry.
Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as