• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.172-177
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• 2006

본 논문은 모바일커머스 사이트의 신뢰도에 영향을 미치는 요인들을 거래안전성, 사이트이미지, 검색기능성, 그리고 결제편의성으로 분류하여 관련 연구들을 통해 검증된 요인들을 추출하였다. 그리고 모바일커머스 사이트에서 이용 경험이 있는 고객들을 대상으로 직접 설문조사를 통해 사이트의 신뢰도 형성에 영향을 미치는 요인들을 식별해내고 구매의도에 어떠한 영향을 미치는 지를 분석한 결과를 바탕으로 고객과의 신뢰에 기반한 모바일커머스의 실제적인 활성화에 기여하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.11-21
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• 2020

This study explores the effect of time-deals, i.e., online promotions with time limit. Recently many online/mobile shopping sites in China utilize so called hot deal marketing technique for a short duration at a specific time. The purpose of "time-deals" is to attract online shoppers with deep discounted price and induce consumers to purchase items. This paper examines the effect of time-deals on sales volume, firstly by comparing the sales volume of time-deal days with that of no time-deal days and secondly by comparing the sales volume of days before and after two types of time deals, usual time-deals and special time-deals, Although some prior research studied the role of time-deals in promoting consumers' purchase behavior, most used the experimental approach by building mock-up shopping sites and asking participants purchase intention. However, purchase intention does not always result in purchase behavior. This study extracted actual purchase data for four items on time-deals from an online shopping site in China. A comparison of sales volumes on time-deal days with no time-deal days has shown the significant difference in the sales volumes. This finding confirms the positive effect of time-deals on purchase behavior. This study has also found that special time-deals soak up near-future demands in advance and lower the sales after the special time-deal event, although there is no significant difference in sales before and after usual time-deals.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.123-131
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• 2020

After observing that most tourists plan to complete their visits to multiple cultural heritage sites within one day, we surmised that for many museum visitors, the foremost thought is with regard to the amount of time is to be spent at each location and how they can maximize their enjoyment at a site while still balancing their travel itinerary? Recommendation systems in e-commerce are built on knowledge about the users' previous purchasing history; recommendation systems for museums, on the other hand, do not have an equivalent data source available. Recent solutions have incorporated advanced technologies such as algorithms that rely on social filtering, which builds recommendations from the nearest identified similar user. Our paper proposes a different approach, and involves providing dynamic recommendations that deploy social filtering as well as content-based filtering using term frequency-inverse document frequency. The main challenge is to overcome a cold start, whereby no information is available on new users entering the system, and thus there is no strong background information for generating the recommendation. In these cases, our solution deploys statistical methods to create a recommendation, which can then be used to gather data for future iterations. We are currently running a pilot test at Chao Samphraya national museum and have received positive feedback to date on the implementation.