• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.582-587
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• 2021

Book recommender system, which suggests book to users according to their book taste and preference effectively improves users' book-reading experience and exposes them to variety of books. Insufficient dataset of book rating records by users degrades the quality of recommendation. In this study, we suggest a book recommendation system that makes use of user's book ratings collaboratively with user's movie ratings where more abundant datasets are available. Through comprehensive experiment, we prove that our methods improve the recommendation quality and effectively recommends more diverse kind of books. In addition, this will be the first attempt for book recommendation system to utilize movie rating data, which is from the media-platform other than books.

• Journal of the Korean Society for information Management
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• v.39 no.2
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• pp.299-324
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• 2022

In this study, in order to explore the contextual elements of book use that were overlooked in the existing book recommender system research, for 15 avid readers with various book search backgrounds, the contents generated in 6 book search situations were collected through the think-aloud protocol. By using content analysis from the collected book use contents, not only the internal and external appeal factors affecting book use, based on the 'appeal factor', the theoretical concept of the readers' advisory service, but also information sources and search methods regarding book use were identified and categorized. The results of this study can be used to extract and reflect meaningful attribute data in the future book recommender system design process.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.59-66
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• 2010

We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.

• Korean Business Review
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• v.20 no.1
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• pp.145-168
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• 2007

Recommender system helps customers to find easily items and helps the e-biz companies to set easily their target customer by automated recommending process. Recommender systems are being adopted by several e-biz companies and from these systems, both of customers and companies take some benefits. This study sets several thresholds to the similarity weight, which indicates a degree of similarity of two customers' preference, to improve the performance of prediction accuracy. According to the threshold, the accuracy of prediction is being improved but some threshold setting shows the reduction of the prediction rate, which is the coverage. This coverage reduction has male effect on the prediction accuracy of customers, so more study on the prediction accuracy of recommender system and to maximize the coverage are needed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.15-23
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• 2009

In ubiquitous environments, invisible devices and software are connected to one another to provide convenient services to users. In this environments, users want to get a variety of customized services by using only an individual mobile device which has limitations such as tiny display screens, limited input, and less powerful processors. Therefore, The device sharing for solving these limitation problems and its efficient processing is one of the new research topics. This paper proposes a device recommender system which searches and recommends devices for composing user requested services. The device recommender system infers devices based on environmental context of a user. However, customized devices for each user are different because of a variety of user preference even if users want to get the same service in the same space, Therefore the paper considers the user preference for device recommendation. Our device recommender system is implemented and tested on the real mobile object developed for device sharing in ubiquitous environments. Therefore we can expect that the system will be adaptable in real device sharing environments.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.254-266
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• 2020

The purpose of this study is to analyze the influence of the type of recommender system on consumer's perceived benefit and privacy risk. The result showed that the perceived usefulness and intension to click was high in the order of Hybrid-filtering, Bestseller, and SNS-based system. Privacy concern was high in order of SNS-based system, Hybrid-filtering, and Bestseller. Moderating effects of perceived personalization on the type of recommender system and perceived usefulness were significant. Finally perceived usefulness had positive effect, and privacy concern had negative effect on consumer's intension to click. This study has significant implications for digital marketing bt comparing consumer responses according to the type of recommended service. The result of this study can be helpful for providing and developing future recommender service.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.298-305
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• 2009

In contrast to content_based filtering systems, collaborative filtering systems not only don't contain information of items, they can not recommend items when users don't provide the information of their interests. In this paper, we propose the recommender agent using association item tree to solve the shortcomings of collaborative filtering systems. Firstly, the proposed method clusters users into groups using vector space model and K-means algorithm and selects group typical rating values. Secondly, the degree of associations between items is extracted from computing mutual information between items and an associative item tree is generated by group. Finally, the method recommends items to an active user by using a group typical rating value and an association item tree. The recommender agent recommends items by combining user information with item information. In addition, it can accurately recommend items to an active user, whose information is insufficient at first rate, by using an association item tree based on mutual information for the similarity between items. The proposed method is compared with previous methods on the data set of MovieLens recommender system.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1213-1226
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• 2008

A recommender system supports people in making recommendations finding a set of people who are likely to provide good recommendations for a given person, or deriving recommendations from implicit behavior such as browsing activity, buying patterns, and time on task. We proposed new recommender system which combined SOM(Self-Organizing Map) neural networks with the Collaborative filtering which most recommender systems hat applied First, we segmented user groups according to demographic characteristics and then we trained the SOM with people's preferences as ito inputs. Finally we applied the classic collaborative filtering to the clustering with similarity in which an recommendation seeker belonged to, and therefore we didn't have to apply the collaborative filtering to the whose data set. Experiments were run for EachMovies data set. The results indicated that the predictive accuracy was increased in terms of MAE(Mean-Absolute-Error).

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.45-63
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• 2007

Recently as ubiquitous environment comes to the fore, information density is raised and enterprise is being able to capture and utilize customer-related information at the same time when the customer purchases a product. In this environment, a need for the recommender systems which can deliver proper information to the customer at the right time and right situation is highly increased. Therefore, the research on recommender systems continued actively in a variety of fields. Until now, most of recommender systems deal with item recommendation. However, in the market in ubiquitous environment where the same item can be purchased at several stores, it is highly desirable to recommend store to the customer based on his/her contextual situation and preference such as store location, store atmosphere, product quality and price, etc. In this line of research, we proposed the store recommender system using customer's contextual situation and preference in the market in ubiquitous environment. This system is based on collaborative filtering and Apriori algorithms. It will be able to provide customer-centric service to the customer, enhance shopping experiences and contribute in revitalizing market in the long term.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.