• The Journal of Information Systems
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• v.22 no.3
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• pp.25-42
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• 2013

Recently various kinds of Information Technology services are created and the quantities of the data flow are increase rapidly. Not only that, but the data patterns that we deal with also slowly becoming diversity. As a result, the demand of discover the meaningful knowledge/information through the various mining analysis such as linkage analysis, sequencing analysis, classification and prediction, has been steadily increasing. However, solving the business problems using data mining analysis does not always concerning, one of the major causes of these limitations is there are some analyzed data can't accurately reflect the real world phenomenon. For example, although the time gap of purchasing the two products is very short, by using the traditional sequencing analysis, the precedence relationship of the two products is clearly reflected. But in the real world, with the very short time interval, the precedence relationship of the two purchases might not be defined. What was worse, the sequence of the purchase intention and the sequence of the purchase realization of the two products might be mutually be reversed. Therefore, in this study, an expanded sequencing analysis methodology has been proposed in order to reflect this situation. In this proposed methodology, the purchases that being made in a very short time interval among the purchase order which might not important will be notice, and the analysis which included the original sequence and reversed sequence will be used to extend the analysis of the data. Also, to some extent a very short time interval can be defined as the time interval, so an experiment were carried out to determine the varying based on the time interval for the actual data.

• Journal of Digital Convergence
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• v.7 no.3
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• pp.75-82
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• 2009

The purpose of this study is explore the effect of brand marketing on the circulation structure factors as corporation image, brand image, and purchase intention and to clarify the causal sequence model in mobile phone corporation The results confirmed the suggested hypotheses. In addition, the analyses showed that effects of both brand marketing-related variables and the circulation structure factors as CI (corporation image) and BI(brand image) and PI(purchase intention) are mediated by the other variables. Based on the findings, the study showed that the effect of brand marketing indirectly on the purchase intention is mediated by corporation image and brand image in mobile phone corporation.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.363-372
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• 2019

A sequence refers to data in which the order exists on the two items, and purchase transaction data in which the products purchased by one customer are listed is one of the representative sequence data. In general, all goods have a product taxonomy, such as category/ sub-category/ sub-sub category, and if they are similar to each other, they are classified into the same category according to their characteristics. Therefore, in this paper, we not only consider the purchase order of products to compare two purchase transaction sequences, but also calculate their similarity by giving a higher score if they are in the same category in spite of their difference. Especially, in order to choose the best similarity measure that directly affects the calculation performance of the purchase transaction sequences, we have compared the performance of three representative similarity measures, the Levenshtein distance, dynamic time warping distance, and the Needleman-Wunsch similarity. We have extended the existing methods to take into account the product taxonomy. For conventional similarity measures, the comparison of goods in two sequences is calculated by simply assigning a value of 0 or 1 according to whether or not the product is matched. However, the proposed method is subdivided to have a value between 0 and 1 using the product taxonomy tree to give a different degree of relevance between the two products, even if they are different products. Through experiments, we have confirmed that the proposed method was measured the similarity more accurately than the previous method. Furthermore, we have confirmed that dynamic time warping distance was the most suitable measure because it considered the degree of association of the product in the sequence and showed good performance for two sequences with different lengths.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Distribution Science
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• v.17 no.8
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• pp.45-56
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• 2019

Purpose - This study aimed at investigating the mediation roles of attitude dimensions in the effects of involvement type on hedonic product purchase intention and moderation role of lay rationalism in the effects of involvement type on attitude dimensions. Research design, data, and Methodology - "Wenjuanxing" was used online to make questionnaire, which was loaded on Wechat and QQ. 125 data were collected online in China. The Process macro model 58 including moderation of the two paths in the causal sequence was used to verify hypotheses. Results and Conclusions - First, cognitive (affective) involvement had positive effect on the utilitarian (hedonic) dimension of consumer attitude and the purchase intention. Second, hedonic dimension of attitude had positive effects on purchase intention, but utilitarian dimension of attitude had not significant positive effects on purchase intention. Third, Lay rationalism did decrease (did not increase) the positive effects of affective (cognitive) involvement on hedonic (utilitarian) dimension of attitude. Therefore Marketing managers should understand the differences between the cognitive involvement and affective involvement, and develop the ways by which they attract consumers to choose their hedonic product. And they should give affective (cognitive) information to the customers with low (high) rationalism consumers when they do marketing for their hedonic product.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.107-123
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• 2021

In an online shopping site or offline store, products purchased by each customer over time form the purchase history of the customer. Also, in most retailers, products have a product taxonomy, which represents a hierarchical classification of products. Considering the product taxonomy, the lower the level of the category to which two products both belong, the more similar the two products. However, there has been little work on similarity measures for sequences considering a hierarchical classification of elements. In this paper, we propose new similarity measures for purchase histories considering not only the purchase order of products but also the hierarchical classification of products. Unlike the existing methods, where the similarity between two elements in sequences is only 0 or 1 depending on whether two elements are the same or not, the proposed method can assign any real number between 0 and 1 considering the hierarchical classification of elements. We apply this idea to extend three existing representative similarity measures for sequences. We also propose an efficient computation method for the proposed similarity measures. Through various experiments, we show that the proposed method can measure the similarity between purchase histories very effectively and efficiently.

• Journal of the Korea Fashion and Costume Design Association
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• v.18 no.2
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• pp.93-100
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• 2016

This paper aimed to provide the basic data on consumers' purchasing tendency required to start and operate online shopping malls on internet. The survey selected the female college students from 19 to 24 years old majoring fabric and fashion design in colleges in Gyeonggi-do. Total 283 questionnaires were selected for statistical analysis. The analysis results are presented below. The first online shopping was during the middle school times showing the highest responses as 63.54%, followed by high school times, college times and elementary school times in that sequence. Most female college students(97.88%) purchased goods from online shopping malls. The purposes of search in online shopping malls were 'need to purchase goods(47.18%)', 'habit/hobbies(27.57%)', 'need to collect data on goods(20.27%)' and 'to relieve stresses(4.98%)'. About 50% of respondents selected 'I visit mainly several online shopping malls. If there is no goods that I try to find, I search other sites and purchase what I want to buy(46.57%).' For the goods purchased from online shopping malls, everyday wears showed the highest ratio, 85.92%. About the time to purchase goods related to trends, most respondents selected 'purchase whenever it is necessary without respect to trends(87%).' Main considerations when the respondents purchased the goods from online shopping malls were 'design(64.98%)', 'price(18.41%)', 'quality(11.20%)', 'company recognition(2.53%)', 'color(1.44%)', and 'materials (1.44%)' in that sequence. 64.62% of respondents had the experience of returning goods after purchasing from online shopping malls. The reason why the respondents returned goods after purchasing from online shopping malls was mainly 'because of size(52.17%)', the response with the highest ratio. 42.24% responded that they experienced damage by washing the goods purchased from online shopping malls. It was found that the respondents didn't think about the country of manufacturing when purchasing goods from online shopping malls.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.207-217
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• 2008

The purpose of this study is explore the effect of brand marketing to corporatei mage, brand image, and purchase intention and so clarify the causal sequence model in mobile phone corporation The results confirmed the suggested hypotheses. In addition, the analyses showed that effects of both brand marketing-related variables and CI on BI or PI are mediated by the other variables. Based on the findings, the study showed that the effect of brand marketing indirectly on the purchase intention is mediated by corporate image and brand image in mobile phone corporation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.331-339
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• 2004

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this paper, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.

• 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.