• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.58-64
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• 2005

Customer sensibility based recommendation agent system was developed to tailor to the customer the suggestion of goods and the description of store catalog in on-line shopping mall. The recommendation agent system composed of five modules and seven services including specialized algorithm. This study was to investigate the effectiveness of the customer sensibility based recommendation agent system in on-line shopping mall. This study asked 30 male and female students to perform the task in on-line shopping mall and facilitated them questionnaires. The questionnaires were administered to subjects to measure quality precision, ease of use, support of buying, purchasing power, future intention of the system. The study revealed that good part of the subjects positively evaluated the customer sensibility based recommendation system except for ease of use. The study on usability of the recommendation agent system has need to be performed in next. This paper shows that the satisfaction and the buying power of customers may be improved by presenting customer sensibility based recommendation in on-line shopping mall.

• Smart Media Journal
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• v.12 no.5
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• pp.9-16
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• 2023

In the recommendation system of the credit card company, it is necessary to understand the customer patterns to predict a customer's next merchant based on their histories. The data we want to model is much more complex and there are various patterns that customers choose. In such a situation, it is necessary to use an effective model that not only shows the relevance of the merchants, but also the relevance of the customers relative to these merchants. The proposed model aims to predict the next merchant for the customer. To improve prediction performance, we propose a novel model, called Customer-based Recommendation Model (CRM), to produce a more efficient representation of customers. For the next merchant recommendation system, we use a synthetic credit card usage dataset, BC'17. To demonstrate the applicability of the proposed model, we also apply it to the next item recommendation with another real-world transaction dataset, IJCAI'16.

• The Journal of Industrial Distribution & Business
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• v.11 no.2
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• pp.17-23
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• 2020

Purpose: The purpose of this study is to understand the effect of the unique product recommendation system on customer satisfaction. Research design, data and methodology: The survey method used the self-recording way in which the respondents selected for the study and distributed 300 questionnaires, and with due personal care, researchers collected all the distributed questionnaires. Results: The result implies that the characteristics of the product recommendation system should be more secure and developed. Conclusions: The aspects of the product recommendation system were selected as factors of price fairness, accuracy, and quality through previous studies, and the empirical analysis of the effect of the characteristics of the product recommendation system on customer satisfaction was summarized as follows. Among the attributes of the product recommendation system, the attributes of price fairness, accuracy, and quality affect customer satisfaction. Among them, the beta value of quality was the highest, and the effect of quality was the largest among the three factors. Based on the results of the study, the implications for the characteristics of the product recommendation system are summarized as follows. The aspects of the product recommendation system have a positive effect on customer satisfaction, so it is necessary to fill the needs of consumers based on the survey focused on quality

• Korean Management Science Review
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• v.23 no.3
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• pp.183-194
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• 2006

In this paper, we propose a customer profiling technique based on customer behavior for personalized products recommendation in Internet shopping mall. The proposed technique defines customer profile model based on customer behavior Information such as click data, buying data, market basket data, and interest categories. We also implement CBCPT(customer behavior based customer profiling technique) and perform extensive experiments. The experimental results show that CBCPT has higher MAE, precision, recall, and F1 than the existing other customer profiling technique.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.1
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• pp.38-46
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• 2004

To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. This paper deals with an intelligent agent approach to incorporate customer's sensibility into an one-to-one recommendation service in on-line shopping mall. In this paper the focus of interest is on-line recommendation service algorithm for development of Human Sensibility based web agent system. The recommendation agent system composed of seven services including specialized algorithm. The on-line recommendation service algorithm use human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system's behavior requires the parallel execution of several tasks during the interaction (e.g., identifying the customer's emotional preference and dynamically generating the pages of the store catalog). Most of the present shopping malls go through the catalog of goods, but the future shopping malls will have the form of intelligent shopping malls by applying the on-line recommendation service algorithm.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.305-314
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• 2002

In order to adapt to today's rapidly changing environment and gain a competitive advantage, many companies are interested in CRM(Customer Relationship Management). Especially, the product recommendation system that can be implemented by personalizing the marketing strategy becomes the focus of CRM. In this research, we employed CBR(Case-Based Reasoning) technique that can overcome the limitation of CF(Collaborative Filtering) technique. Our system recommends the books that the customer is very likely to buy next time considering the factors such as 'Personal Features of Customer,' Similarity between Book Categories' and 'Sequence of Book Purchases'. Accuracy of predicting a book-not a particular book, but in the middle level of classification that contains about 190 categories-was about 57%.

• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.592-600
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• 2004

A recommendation system, which is an application area of e-CRM in e-commerce environment, provides individualized goods recommendation service that meets the demand of individual users. In general, existing recommendation systems require extensive historic user information in application domains. However, the method of recommendation based on static historic user information needs to respond flexibly to users'demand that changes rapidly and sensitively over time and in domains including a variety of users. In addition, it is difficult to recommend for new users who are not fall into any of existing domains. To overcome such limitations and provide flexible recommendation service, this study designed and implemented CPAR (Customer Preference Analysis Recommender) system that supports customer preference analysis and filter management. The filtering management capacity of the present system eases the necessity of extensive information about new users. In addition, CPAR system was implemented in XML-based wireless Internet environment for recommendation service independent from platforms and not limited by time and place.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.2
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• pp.27-39
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• 2006

The Collaborative Filtering (CF) is one of the popular techniques for personalization in e-commerce storefronts. For CF-based recommendation, every customer needs to provide subjective evaluation ratings for some products based on his/her preference. Also, if an e-commerce site recommends a new product, some customers should rate it. However, there is no in-depth investigation on the impacts on recommendation performance of two number of ratings, i.e. the number of ratings of an individual customer and the number of ratings of an item, even though these are important factors to determine performance of CF methods. In this study, using publicly available EachMovie data set, we empirically investigate the relationships between the two number of ratings and the performance of CF. For the purpose, three analyses were executed. The first and second analyses were performed to investigate the relationship between the number of ratings of a particular customer and the recommendation performance of CF. In the third analysis, we investigate the relationship between the number of ratings on a particular item and the recommendation performance of CF. From these experiments, we can find that there are thresholds in terms of the number of ratings below which the recommendation performances increase monotonically. That is, the number of ratings of a customer and the number of ratings on an item are critical to the recommendation performance of CF when the number of ratings is less than the thresholds, but the value of the ratings decreases after the numbers of ratings pass the thresholds. The results of the experiments provide insight to making operational decisions concerning collaborative filtering in practice.

• Information Systems Review
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• v.18 no.2
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• pp.59-77
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• 2016

Although various recommendation techniques have been applied to the e-commerce market, few studies compare the intent to use these techniques from the customer's perspective. In this paper, we conduct a comparative analysis of customers' intention to use five recommendation techniques widely adapted by online shopping malls and focus on the differences in purchasing electronic goods and apparel products. The recommendation techniques are as follows: best-seller recommendation, merchandiser recommendation, content-based recommendation, collaborative filtering recommendation, and social recommendation. Additionally, we examine which factors influence customer intent to use the recommendation services. Data were collected through a survey administered to 220 e-commerce users with prior experience with recommendation services. Collected data were examined using analysis of variance and regression analysis. Results indicate statistically significant differences in customers' intention to use recommendation services according to the recommendation technique. In particular, the best-seller recommendation technique is preferred when purchasing electronic goods, whereas the content-based recommendation technique is preferred for apparel purchases. Factors such as personal characteristics and personality, purchasing tendency, as well as perception of the product or recommendation service affect a customer's intention to use a recommendation service. However, the influence of these factors varies depending on the recommendation technique. This study provides guidelines for companies to adopt appropriate recommendation techniques according to product categories and personal characteristics of customers.