• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.562-565
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• 2010

본 논문에서는 RFID 태그를 이용하여 고객의 위치를 인식할 수 있는 개체 인식 기술과 고객의 현재 위치 및 쇼핑 동선파악을 위한 데이터 무선 전송 및 저장 기술, 마지막으로 고객화된 정보를 자동으로 생성하고 적시에 해당 고객에게 제공해 줄 유비쿼터스형 에이전트 기술을 적용하여 쇼핑 선호 상품추천 시스템을 설계 및 구현 하고자 한다.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.437-441
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• 2006

본 논문에서는 RFID 위치추적엔진과 지능형 에이전트를 이용한 선호상품 추천 기법을 이용하여 RFID기반 위치추적을 이용한 실시간 선호 상품 추천 시스템을 제안한다. 매장안에서 RFID 태그가 부착된 스마트 카트를 이용하여 고객의 위치를 실시간으로 파악하여 각 구역별 쇼핑시간과 개별 고객의 구매 히스토리 분석 및 이동 구역 예측을 통해 실시간으로 쇼핑 매장에서 각 고객의 선호상품을 추천한다.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.91-94
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• 2008

정보 기술의 발전으로 인해 이용할 수 있는 정보가 기하급수적으로 늘어남에 따라, 사용자는 원하는 정보를 얻는 데 어려움을 겪게 되고, 양질의 정보를 찾기 위해 많은 시간을 들이고 있다. 이에 사용자의 의도를 정확하고 명백하게 드러내는 태그 정보에 기반한 협력적 필터링 기법을 이용하여 사용자가 원하는 적절한 음악을 추천하는 시스템을 제안하며, 태그의 확장을 통한 협력적 필터링 기법의 성능 향상을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.1119-1121
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• 2014

저널 논문 투고 및 심사시스템에서의 논문 제출은 상시 이루어진다는 특성 때문에 논문이 제출된 시점에 적절한 심사자들을 찾아 배정하기란 쉽지 않은 문제이다. 본 논문에서는 이러한 문제를 해결하기 위하여 제출된 논문에 적절한 심사자들을 추천해주는 알고리즘을 제시하고자 한다. 심사자 추천 알고리즘에서는 해당 논문의 전문가를 심사자로써 추천하기 위하여 제출된 논문들의 키워드(Keyword)와 심사자들의 전문지식태그(Expertise Tag) 정보를 활용한다. 또한 심사자들의 기존의 심사 정보를 토대로 심사활동지수를 평가하여 이를 심사자 추천에 활용하고자 한다. 제안하는 알고리즘을 검증하기 위하여 본 논문에서는 실제 저널 논문투고시스템에 추천 알고리즘을 적용해보고 이의 결과를 제시한다.

• Journal of Broadcast Engineering
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• v.16 no.2
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• pp.237-246
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• 2011

In order to produce the variety of UCC, the recommendation service is required which considers the copyright of UCC producer discriminated from one for UCC consumers and the purpose of its production. The recommender system designed in this thesis enables UCC which is much similar to one UCC producer utilizes to be used with custom-made when recommending and producing based on UCC view history and production list, etc. of its producer. The recommender system is largely divided into filtering based on the preferred tag, UCC filtering used when producing the preferred UCC and creating process of recommended UCC using the Pearson formula. The recommender system in this thesis requires the data which were used when producing UCC. For that, we added the reference factor so that the data of UCC which were utilized when producing UCC into the existing metadata can be recorded. If the recommender system suggested in this thesis is used, the more effective and convenient UCC recommendation services with custom-made for producers can be provided.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.11-24
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• 2019

The tendency of buyers of web information is changing from the cost-effectiveness to the cost-satisfaction. There is such tendency in the recommendation of multimedia contents, some of which are folksonomy-based recommendation services using mood. However, there is a problem that they does not consider synonyms. In order to solve this problem, some studies have solved the problem by defining 12 moods of Thayer model as AV values (Arousal and Valence), but the recommendation performance is lower than that of a keyword-based method at the recall level 0.1. In this paper, we propose a method based on using mood vector of multimedia contents. The method can solve the synonym problem while maintaining the same performance as the keyword-based method even at the recall level 0.1. Also, for performance analysis, we compare the proposed method with an existing method based on AV value and a keyword-based method. The result shows that the proposed method outperform the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1183-1190
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• 2017

With the development of network and IT technology, people are searching and purchasing items they want, not bounded by places. Therefore, there are various studies on how to solve the scalability problem due to the rapidly increasing data in the recommendation system. In this paper, we propose an item-based collaborative filtering method using Tag weight and a recommendation technique using MapReduce method, which is a distributed parallel processing method. In order to improve speed and efficiency, the proposed method classifies items into categories in the preprocessing and groups according to the number of nodes. In each distributed node, data is processed by going through Map-Reduce step 4 times. In order to recommend better items to users, item tag weight is used in the similarity calculation. The experiment result indicated that the proposed method has been more enhanced the appropriacy compared to item-based method, and run efficiently on the large amounts of data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1911-1916
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• 2013

One significant challenge that arises in social tagging systems is the rapid increase in the number and diversity of the tags. As opposed to structured annotation systems, tags provide users an unstructured, open-ended mechanism to annotate and organize web-content. In this paper, we propose a scheme for URL recommendation that is based on a folksonomy which is comprised of user-defined tags, URL-keywords and the category folder name as the major element. This scheme will be further improved and implemented on a browser extension that recommends to users the best way to classify a particular URL.