• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.349-352
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• 2000

As a lot of people have used electronic commerce, many shopping malls have appeared on the Interne and the shopping information in them has been enormous. So, the need for a system to recommend product to customers is on the increase so as to reduce time and efforts for shopping. In this paper, we suppose a Product Recommender System which is constructed by applying data mining techniques to web for files and analyzing customer's action pattern, customer's profile and product purchase data. This system offers convenience that customers can get their desired information easily, by sending e-mail or mail and recommending web pages when they visit a shopping mall.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.45-50
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• 2018

Many of the current successful commercial recommender systems utilize collaborative filtering techniques. This technique recommends products to the active user based on product preference history of the neighbor users. Those users with similar preferences to the active user are typically named his/her neighbors. Hence, finding neighbors is critical to performance of the system. Although much effort for developing similarity measures has been devoted in the literature, there leaves a lot to be improved, especially in the aspect of handling subjectivity or vagueness in user preference ratings. This paper addresses this problem and presents a novel similarity measure using fuzzy logic for selecting neighbors. Experimental studies are conducted to reveal that the proposed measure achieved significant performance improvement.

• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.97-113
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• 2009

Collaborative filtering(CF) has been a very successful approach for building recommender system, but its widespread use has exposed to some well-known problems including sparsity and scalability problems. In order to mitigate these problems, we propose two novel models for improving the typical CF algorithm, whose names are ISCF(Item-Selected CF) and USCF(User-Selected CF). The modified models of the conventional CF method that condense the original dataset by reducing a dimension of items or users in the user-item matrix may improve the prediction accuracy as well as the efficiency of the conventional CF algorithm. As a tool to optimize the reduction of a user-item matrix, our study proposes genetic algorithms. We believe that our approach may relieve the sparsity and scalability problems. To validate the applicability of ISCF and USCF, we applied them to the MovieLens dataset. Experimental results showed that both the efficiency and the accuracy were enhanced in our proposed models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.416-425
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering (CF) has been known to be the most successful recommendation technology. However its widespread use in e-commerce has exposed two research issues, sparsity and scalability. In this paper, we propose several hybrid recommender procedures based on web usage mining, clustering techniques and collaborative filtering to address these issues. Experimental evaluation of suggested procedures on real e-commerce data shows interesting relation between characteristics of procedures and diverse situations.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.329-336
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• 2002

With the fast development of Internet environment, e-Commerce is rapidly increasing. n the expanding e-Commerce environment, the need for a new e-Commerce systems what will deliver products to the customer rapidly and increase sales is growing bigger. Recently, these requirements brought many researches on recommender systems. However, until now, those recommender systems have a limit because it takes too much time for recommender systems to give customers the recommendations in real time, if the number of purchase data of customers is large. So this paper concerns on the recommender systems using collaborative filtering as one of the solutions to increase the competitive power. We proposed and experimented the more improved recommender systems which could decrease the data size to shorten the recommending time by using the representative category of the product which customers want to buy. Also, we design and implement a recommender system using Enterprise JavaBeans.

• KIISE Transactions on Computing Practices
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• v.22 no.12
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• pp.646-653
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• 2016

Outbound market is a rapidly growing global industry, and has evolved into a 11 trillion won trade. A lot of recommender systems, which are based on collaborative and content filtering, target the existing purchase log or rely on studies based on similarity of products. These researches are not highly efficient as data was not obtained in advance, and acquiring the overwhelming amount of data has been relatively slow. The characteristics of an outbound product are that it should be purchased at least twice in a year, and its pricing should be in the higher category. Since the repetitive purchase of a product is rare for the outbound market, the old recommender system which profiles the existing customers is lacking, and has some limitations. Therefore, due to the scarcity of data, we suggest an improved customer-profiling method using web usage mining, algorithm of association rule, and rule-based algorithm, for faster recommender system of outbound product.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.510-514
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• 2004

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation methodology based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of original CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real e-commerce data show that the proposed methodology provides higher quality recommendations and better performance than original collaborative filtering methodology.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.139-157
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• 2002

The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.5
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• pp.541-550
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• 2003

It is important for the strategy of product sales to investigate the consumer's sensitivity and preference degree in the environment that the process of material development has been changed focusing on the consumer renter. In the present study, we propose the Fashion Design Recommender System (FDRS) of textile design applying collaborative filtering personalization technique as one of methods in the material development centered on consumer's sensibility and preferences. In collaborative filtering personalization technique based on textile, Pearson Correlation Coefficient is used to calculate similarity weights between users. We build the database founded on the sensibility adjective to develop textile designs by extracting the representative sensibility adjective from users' sensibility and preferences about textile designs. FDRS recommends textile designs to a consumer who has a similar propensity about textile. Ultimately, this paper sugeests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommender System (FDRS)