• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.40-42
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• 2022

People's focus steadily shifted toward fashion as a popular aesthetic expression as their quality of life improved. Humans are inevitably drawn to things that are more aesthetically appealing. This human proclivity has resulted in the evolution of the fashion industry over time. However, too many clothing alternatives on e-commerce platforms have created additional obstacles for clients in recognizing their suitable outfit. Thus, in this paper, we proposed a personalized Fashion Recommender system that generates recommendations for the user based on their previous purchases and history. Our model aims to generate recommendations using an image of a product given as input by the user because many times people find something that they are interested in and tend to look for products that are like that. In the system, we first reduce data dimensionality by component analysis to avoid the curse of dimensionality, and then the final suggestion is generated by neural network. To create the final suggestions, we have employed neural networks to evaluate photos from the H&M dataset and a nearest neighbor backed recommender.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.19-22
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• 2022

서비스 관점에서 구축되는 추천 시스템의 성능은 얼마나 효율적인 추천 모델을 적용하여 심층적으로 설계되었는가에 좌우된다고도 볼 수 있다. 특히, 추천 시스템의 초개인화는 세계적인 추세로 1~2년 전부터 구글, 아마존, 알리바바 등의 데이터 플랫폼 강자들이 경쟁적으로 딥 러닝 기반의 알고리즘을 개발, 자신들의 추천 서비스에 적용하고 있다. 본 연구는 갈수록 고도화되는 추천 시스템으로 인해 발생하는 여러 문제들 중 사용자 또는 서비스 정보가 부족하여 계속적으로 발생하고 있는 Cold-start 문제와 추천할 서비스와 사용자는 지속적으로 늘어나지만 실제로 사용자가 소비하게 되는 서비스의 비율은 현저하게 감소하는 데이터 희소성 문제 (Sparsity Problem)에 대한 솔루션을 모색하는 알고리즘 관점에서 연구하고자 한다. 본 논문은 첫 단계로, 적용하는 메타데이터에 따라 추천 결과의 정확성이 얼마나 차이가 나는지를 보이고 딥러닝 비지도학습 방식을 메타데이터 선정 및 추출에 적용하여 실시간으로 변화하는 소비자의 실제 생활 패턴 및 니즈를 예측해야 하는 필요성에 대해서 기술하고자 한다.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.129-139
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• 2012

This paper introduces a recommender system that is to recommend broadcast content. Our recommender system uses user's viewing history for personalized recommendations. Broadcast contents has unique characteristics as compared with books, musics and movies. There are two types of broadcast content, a series program and an episode program. The series program is comprised of several programs that deal with the same topic or story. Meanwhile, the episode program covers a variety of topics. Each program of those has different topic in general. Therefore, our recommender system recommends TV programs to users according to the type of broadcast content. The recommendations in this system are based on user's viewing history that is used to calculate content similarity between contents. Content similarity is calculated by exploiting collaborative filtering algorithm. Our recommender system uses java sparse array structure and performs memory-based processing. And then the results of processing are stored as an index structure. Our recommender system provides recommendation items through OPEN APIs that utilize the HTTP Protocol. Finally, this paper introduces the implementation of our recommender system and our web demo.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.89-103
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• 2019

Recently, various approaches to recommendation systems have been studied in terms of the quality of recommendation system. A recommender system basically aims to provide personalized recommendations to users for specific items. Most of these systems always recommend the most relevant items of users or items. Traditionally, the evaluation of recommender system quality has focused on the various predictive accuracy metrics of these. However, recommender system must be not only accurate but also useful to users. User satisfaction with recommender systems as an evaluation criterion of recommender system is related not only to how accurately the system recommends but also to how much it supports the user's decision making. In particular, highly serendipitous recommendation would help a user to find a surprising and interesting item. Serendipity in this study is defined as a measure of the extent to which the recommended items are both attractive and surprising to the users. Therefore, this paper proposes an application of serendipity measure to recommender systems to evaluate the performance of recommender systems in terms of recommendation system quality. In this study we define relevant or attractive unexpectedness as serendipity measure for assessing recommendation systems. That is, serendipity measure is evaluated as the measure indicating how the recommender system can find unexpected and useful items for users. Our experimental results show that highly serendipitous recommendation such as item-based collaborative filtering method has better performance than the other recommendations, i.e. user-based collaborative filtering method in terms of recommendation system quality.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.35-43
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• 2017

In IoT(Internet of Things) environment, users want to receive customized service by users' personal device such as smart watch and pendant. To fulfill this requirement, the mobile device should support a lot of functions. However, the miniaturization of mobile devices is another requirement and has limitation such as tiny display. limited I/O, and less powerful processors. To solve this limitation problem and provide customized service to users, this paper proposes a collaboration system for sharing various computing resources. The paper also proposes the method for reasoning and recommending suitable resources to compose the user-requested service in small device with limited power on expected time. For this goal, our system adopts MBTI(Myers-Briggs Type Indicator) to analyzes user's behavior pattern and recommends personalized resources based on the result of the analyzation. The evaluation in this paper shows that our approach not only reduces recommendation time but also increases user satisfaction with the result of recommendation.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.467-477
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• 2004

While recommender systems were used by a few E-commerce sites former days, they are now becoming serious business tools that are re-shaping the world of I-commerce. And collaborative filtering has been a very successful recommendation technique in both research and practice. But there are two problems in personalized recommender systems, it is First-Rating problem and Sparsity problem. In this paper, we solve these problems using the associative relation clustering and “Lift” of association rules. We produce “Lift” between items using user's rating data. And we apply Threshold by -cut to the association between items. To make an efficiency of associative relation cluster higher, we use not only the existing Hypergraph Clique Clustering algorithm but also the suggested Split Cluster method. If the cluster is completed, we calculate a similarity iten in each inner cluster. And the index is saved in the database for the fast access. We apply the creating index to predict the preference for new items. To estimate the Performance, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.95-117
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• 2022

With the emergence of various media types due to the development of Internet technology, advertisers have difficulty choosing media suitable for corporate advertising strategies. There are challenging to effectively reflect consumer contextual information when advertising media is selected based on traditional marketing strategies. Thus, a recommender system is needed to analyze consumers' past data and provide advertisers with personalized media based on the information consumers needs. Since the traditional recommender system provides recommendation services based on quantitative preference information, there is difficult to reflect various contextual information. This study proposes a methodology that uses deep learning to recommend personalized media to advertisers using consumer contextual information such as consumers' media viewing time, residence area, age, and gender. This study builds a recommender system using media & consumer research data provided by the Korea Broadcasting Advertising Promotion Corporation. Additionally, we evaluate the recommendation performance compared with several benchmark models. As a result of the experiment, we confirmed that the recommendation model reflecting the consumer's contextual information showed higher accuracy than the benchmark model. We expect to contribute to helping advertisers make effective decisions when selecting customized media based on various contextual information of consumers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.55-59
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• 2002

인터넷 및 전자상거래의 급속한 발전에 따라, 전자상거래를 위한 수많은 상품정보가 생성, 수정, 삭제되고 있는 상황에서 소비자들을 위한 맞춤형 정보서비스 및 개별화된 상품추천 시스템에 대한 필요성이 증가되고 있고 많은 연구가 이루어지고 있다. 그러므로 본 논문에서는 이러한 요구를 수용할 수 있는 소비자 지향형 상품추천 시스템을 제안한다. 제안된 시스템은 사용자행위의 모니터 링을 통해 사용자의 관심분야 및 다수의 사용자가 관심을 가지는 상품정보를 추출하며 이를 기반으로 사용자에게 추천함으로써 양질의 정보 및 서비스의 제공에 있다.