• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.10-20
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• 2024

As the online commerce market continues to expand with an increase of diverse products and content, users find it challenging in navigating and in the selection process. Thereafter both platforms and shopping malls are actively working in conducting continuous research on recommendations system to select and present products that align with user preferences. Most existing recommendation studies have relied on user data which is relatively easy to obtain. However, these studies only use a single type of event and their reliance on time dependent data results in issues with reliability and complexity. To address these challenges, this paper proposes a recommendation system that analysis user preferences in consideration of the relationship between various types of event data. The proposed recommendation system analyzes the correlation of multiple events, extracts weights, learns the recommendation model, and provides recommendation services through it. Through extensive experiments the performance of our system was compared with the previously studied algorithms. The results confirmed an improvement in both complexity and performance.

• Korean small business review
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• v.41 no.1
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• pp.53-73
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• 2019

Recently, as the number of shopping facilities has increased, such as complex shopping malls, warehouse type superstores, large fashion outlets, and so on, the conflicts over the opening of large stores between neighboring municipalities are increasing. However, current regulations on the opening of large-scale stores, such as the impact analysis on commercial area, do not adequately reflect the characteristics of new type shopping facilities. In this study, we tried to suggest a rational policy alternative with more realistic suitability by analyzing the characteristics of 'regional shopping facilities' beyond the scope of the municipalities, and analyzing the impact on the regional merchants. The main results of the study are summarized as follows. First, unlike previous researches, which are limited to small business sector, this study presents the results of comprehensively comparing and analyzing the impact on the detailed sectors of the whole distribution market, including the large distribution sector and online distribution sector. Second, in this study, we calculated the total (average) amount of market penetration rate of existing shopping facilities by the entire regional shopping facilities in the Seoul metropolitan area, and this is considered to be of great value in relation to the recognition of problems at the whole level of the metropolitan area and the search for alternative solutions.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.1109-1113
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• 2009

Exchange of data between users and system is made by users' inputting their information into HTML input forms like when signing up in websites or buying in online shopping malls. If users make a mistake of inputting their information, the system gives an error message in order for users to correct their mistakes. Although ISO recommends immediate feedback, Bargas et al. gave a report that feedback in all after filling forms out is more usable than feedback one by one when inputting data online. We made a comparative experiment on usability under each case of feedback one by one and feedback in all. The result is immediate feedback is more usable and satisfactory. The analysis of data resulted that immediate feedback produced fewer error and gets higher subjective ratings.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.2
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• pp.47-57
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• 2013

Today a lot of functional areas of a firm are operated on the Web. Online shopping malls analyze web log recording customers' activities on the web to connect them to business outcomes. Not only commercial websites, but online news sites also need to collect and analyze web logs to understand their news readers' interest. However, little research has been performed yet. In this research we mined the web access log of an online news website and conduct Market Basket Analysis to uncover the association rules among the categories of news articles. The research is composed of two stages: 1) Identifying the individual session of a visitor; 2) Mining association rule from news articles read by each session. We gather 7-day access logs two times. The results of log mining and meanings of association rules are suggested with managerial implications in conclusion section.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Fashion & Textile Research Journal
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• v.15 no.5
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• pp.743-752
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• 2013

Consumers can share their diverse opinions about products due to the increased popularity of smartphones and the internet that enables increased access to the websites and encourages the use of 'SNS' (Social Network Services). Social commerce has expanded rapidly as a mainstream online shopping channel over the past year, compelling consumers to spend more money vis-$\grave{a}$-vis social commerce websites. This allows individuals to see different lists of prices for specific products; however, it is also likely to lead to serious compulsive buying. Despite these changing trends, earlier studies have been limited to factors that affect impulse purchases vis-$\grave{a}$-vis Internet shopping malls; therefore, in is salient to conduct research on consumer spending habits related to social commerce websites. This study describes the characteristics of consumers and utilizes social commerce websites as parameters for the impact of impulsive shopping based on a survey of 300 women in their 20's who have experienced impulse buying vis-$\grave{a}$-vis social commerce websites. This study shows that: First, discounts, product scarcity and social commerce site's reputation affect impulse buying when consumers buy products from the social commerce websites. Second, variables on the characteristics of the personal experiences, interests, and self-control also stimulate impulsive purchases.

• Journal of Distribution Research
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• v.16 no.2
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• pp.1-24
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• 2011

1. Introduction: Contrast to the offline purchasing environment, online store cannot offer the sense of touch or direct visual information of its product to the consumers. So the builder of the online shopping mall should provide more concrete and detailed product information(Kim 2008), and Alba (1997) also predicted that the quality of the offered information is determined by the post-purchase consumer satisfaction. In practice, many fashion and apparel online shopping malls offer the picture information with the product on the real person model to enhance the usefulness of product information. On the other virtual product experience has been suggested to the ways of overcoming the online consumers' limited perceptual capability (Jiang & Benbasat 2005). However, the adoption and the facilitation of the virtual reality tools requires high investment and technical specialty compared to the text/picture product information offerings (Shaffer 2006). This could make the entry barrier to the online shopping to the small retailers and sometimes it could be demanding high level of consumers' perceptual efforts. So the expensive technological solution could affects negatively to the consumer decision making processes. Nevertheless, most of the previous research on the online product information provision suggests the VR be the more effective tools. 2. Research Model and Hypothesis: Presented in