• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.183-188
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• 2021

Collaborative filtering based recommender systems are currently indispensable function of commercial systems in various fields, being a useful service by providing customized products that users will prefer. However, there is a high possibility that the prediction of preferrable products is inaccurate, when the user's rating data are insufficient. In order to overcome this drawback, this study suggests a stepwise method for prediction of product ratings. If the application conditions of the prediction method corresponding to each step are not satisfied, the method of the next step is applied. To evaluate the performance of the proposed method, experiments using a public dataset are conducted. As a result, our method significantly improves prediction and precision performance of collaborative filtering systems employing various conventional similarity measures and outperforms performance of the previous methods for solving rating data sparsity.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.293-301
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• 2017

Most new products not only suddenly disappear in the market but also quickly cannibalize older products. Under such a circumstance, retailers may have too much stock, and customers may be faced with difficulties discovering products suitable to their preferences among short life cycle products. To address these problems, recommender systems are good solutions. However, most previous recommender systems had difficulty in reflecting changes in customer preferences because the systems employ static customer preferences. In this paper, we propose a recommendation methodology that considers dynamic customer preferences. The proposed methodology consists of dynamic customer profile creation, neighborhood formation, and recommendation list generation. For the experiments, we employ a mobile image transaction dataset that has a short product life cycle. Our experimental results demonstrate that the proposed methodology has a higher quality of recommendation than a typical collaborative filtering-based system. From these results, we conclude that the proposed methodology is effective under conditions where most new products have short life cycles. The proposed methodology need to be verified in the physical environment at a future time.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.79-80
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• 2007

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.1010-1014
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• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.83-89
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• 2022

As a representative technique of recommender systems, collaborative filtering has been successfully in service through many commercial and academic systems. This technique recommends items highly rated by similar neighbor users, based on similarity of ratings on common items rated by two users. Recently research on time-aware recommender systems has been conducted, which attempts to improve system performance by reflecting user rating time of items. However, the decay rate uniform to past ratings has a risk of lowering the rating prediction performance of the system. This study proposes a rating time-aware similarity measure between users, which is a novel approach different from previous ones. The proposed approach considers changes of similarity value over time, not item rating time. In order to evaluate performance of the proposed method, experiments using various parameter values and types of time change functions are conducted, resulting in improving prediction accuracy of existing traditional similarity measures significantly.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.31-50
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• 2009

As the collected information which is static or dynamic is infinite in ubiquitous computing environments, information overload and invasion of privacy have been pressing issues in the recommendation service. In this study, we propose a recommendation service procedure through P2P, The P2P helps customer to obtain effective and secure product information because of communication among customers who have the similar preference about the products without connection to server. To evaluate the performance of the proposed recommendation service, we utilized real transaction and product data of the Korean mobile company which service character images. We developed a prototype recommender system and demonstrated that the proposed recommendation service makes an effect on recommending product in the ubiquitous environments. We expect that the information overload and invasion of privacy will be solved by the proposed recommendation procedure in ubiquitous environment.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.9-16
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• 2016

Collaborative filtering(CF) uses the purchase or item rating history of other users, but does not need additional properties or attributes of users and items. Hence CF is known th be the most successful recommendation technology. But conventional CF approach has some significant weakness, such as the new user problem. In this paper, we propose a approach using word embedding with skip-gram for learning distributed item representations. In particular, we show that this approach can be used to capture precise item for solving the "new user problem." The proposed approach has been tested on the Movielens databases. We compare the performance of the user based CF, item based CF and our approach by observing the change of recommendation results according to the different number of item rating information. The experimental results shows the improvement in our approach in measuring the precision applied to new user problem situations.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.276-279
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• 2004

The location-based service is one of the most powerful services in the mobile area. The location-based service provides information service for moving user's location information and information service using wire / wireless communication. In this paper, we propose a model for personalized recommendation system which includes location information and personalized recommendation system for location-based service. For this service system, we consider mobile clients that have a limited resource and low bandwidth. Because it is difficult to input the words at mobile device, we must deliberate it when we design the interface of system. We design and implement the personalized recommendation system for location-based services(advertisement, discount news, and event information) that support user's needs and location information. As a result, it can be used to design the other location-based service systems related to user's location information in mobile environment. In this case, we need to establish formal definition of moving objects and their temporal pattern.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• 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.