• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.10-16
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• 2015

In this study, we suggest a method to predict probability distribution of a new customer's degree of loyalty using C-CRF that reflects the RFM score and similarity to the neighbors of the customer. An RFM score prediction model is introduced to construct the first feature function of C-CRF. Integrating demographical similarity, purchasing characteristic similarity and purchase history similarity, we make a unified similarity variable to configure the second feature function of C-CRF. Then parameters of each feature function are estimated and we train our C-CRF model by training data set and suggest a probabilistic distribution to estimate a new customer's degree of loyalty. An example is provided to illustrate our model.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• The Journal of Industrial Distribution & Business
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• v.10 no.10
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• pp.53-65
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• 2019

Purpose - Current study aimed at investigating the effects of the choice easiness as a thought triggered at the time of making decision and the goal achievement emotion as a prediction of how consumers feel in the state of achieving consumption goal on brand purchase intention. And It also explored moderation role of incidental pride type such as ambient hubris pride and ambient authentic pride felt before the event in the effects of message type such as self-verifying message and self-enhancing message on the choice easiness and the goal achievement emotion. Research design, data, and methodology - Message type was divided into self-verifying message and self-enhancing message. Incidental pride type was divided into hubris and authentic pride. Smart mobile phone was selected for empirical study. And the experiment was performed with 2(pride type: hubristic versus authentic) × 2(message type: self-verifying message versus self-enhancing message) between-subjects design. Questionnaires from 215 undergraduate students were used to test hypotheses by Macro process model 7. The hypotheses were tested at each of self-verifying message group and self-enhancing message group. Results - First, both choice easiness and goal achievement emotion positively influenced on the purchase intention at both self-verifying message group and self-enhancing message group. Second, at self-verifying message group, the positive effects of self verification on both choice easiness and goal achievement emotion were higher to the customers under incidental hubris pride than to those under incidental authentic pride customers. Third, at self-enhancing message group, the positive effects of self enhancement on goal achievement emotion were higher to the customers under incidental authentic pride than to those under incidental hubris pride. However, at self-enhancing message group, the positive effects of self enhancement on choice easiness (goal achievement emotion) were not higher (higher) to the customers under incidental authentic pride than to those under incidental hubris pride. Conclusions - Focusing on the results of this study, to promote their brand purchase intention, brand managers should use self-enhancing message to induce goal achievement emotion from incidental authentic pride customers. And the brand managers should develop and use self-verifying message to induce choice easiness as well as goal achievement emotion from hubris pride customers, which in turn, promote their brand purchase intention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• The Journal of Information Systems
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• v.26 no.3
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• pp.47-61
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• 2017

Purpose Although automobile production has increased since the development of the Korean automobile industry, the number of customers who can purchase automobiles decreases relatively. Therefore, automobile companies need to develop strategies to attract customers and promote their repurchase behaviors. To this end, this paper analyzed customer data from a Korean automobile company using data mining techniques to derive repurchase strategies. Design/methodology/approach We conducted under-sampling to balance the collected data and generated 10 datasets. We then implemented prediction models by applying a decision tree, naive Bayesian, and artificial neural network algorithms to each of the datasets. As a result, we derived 10 patterns consisting of 11 variables affecting customers' decisions about repurchases from the decision tree algorithm, which yielded the best accuracy. Using the derived patterns, we proposed helpful strategies for improving repurchase rates. Findings From the top 10 repurchase patterns, we found that 1) repurchases in January are associated with a specific residential region, 2) repurchases in spring or autumn are associated with whether it is a weekend or not, 3) repurchases in summer are associated with whether the automobile is equipped with a sunroof or not, and 4) a customized promotion for a specific occupation increases the number of repurchases.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.113-125
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• 2017

Customers have been affected by others' opinions when they make a purchase. Thanks to the development of technologies, people are sharing their experiences such as reviews or ratings through online or social network services, However, although ratings are intuitive information for others, many reviews include only texts without ratings. Also, because of huge amount of reviews, customers and companies can't read all of them so they are hard to evaluate to a product without ratings. Therefore, in this study, we propose a methodology to predict ratings based on reviews for a product. In a methodology, we first estimate the topic-review matrix using the Latent Dirichlet Allocation technic which is widely used in topic modeling. Next, we predict ratings based on the topic-review matrix using the artificial neural network model which is based on the backpropagation algorithm. Through experiments with actual reviews, we find that our methodology can predict ratings based on customers' reviews. And our methodology performs better with reviews which include certain opinions. As a result, our study can be used for customers and companies that want to know exactly a product with ratings. Moreover, we hope that our study leads to the implementation of future studies that combine machine learning and topic modeling.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• Journal of Smart Tourism
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• v.1 no.1
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• pp.53-64
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• 2021

Upselling is often a critical factor in revenue generation for businesses in the tourism and travel industry. Utilizing passenger data from a major international airline company, we develop the PAX (Passenger, Airline, eXternal) model to predict passengers that are most likely to accept an upgrade offer from economy to premium. Formulating the problem as an extremely unbalanced, cost-sensitive, supervised binary classification, we predict if a customer will take an upgrade offer. We use a feature vector created from the historical data of 3 million passenger records from 2017 to 2019, in which passengers received approximately 635,000 upgrade offers worth more than $422,000,000 U.S. dollars. The model has an F1-score of 0.75, outperforming the airline's current rule-based approach. Findings have several practical applications, including identifying promising customers for upselling and minimizing the number of indiscriminate emails sent to customers. Accurately identifying the few customers who will react positively to upgrade offers is of paramount importance given the airline 'industry's razor-thin margins. Research results have significant real-world impacts because there is the potential to improve targeted upselling to customers in the airline and related industries.