• 제목/요약/키워드: item classification based on preference

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Preference Difference Metric을 이용한 아이템 분류방식의 추천알고리즘 (Recommendation Algorithm by Item Classification Using Preference Difference Metric)

  • 박찬수;황태규;홍정화;김성권
    • 정보과학회 컴퓨팅의 실제 논문지
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    • 제21권2호
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    • pp.121-125
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    • 2015
  • 기존의 협업필터링 기반의 추천시스템에 대한 연구는 정확한 평점예측에 집중되면서 추천시스템의 수행시간이 길어지게 되고, 선호아이템을 짧은 시간에 추천해주는 본래의 목적에서 멀어지게 되었다. 본 논문에서는 Preference Difference Metric을 이용하여 평점예측이 아닌 선호 아이템의 분류를 통한 추천을 수행하여 수행시간을 단축하고 정확도를 유지하는 추천 알고리즘을 제안한다.

CLASSIFICATION FUNCTIONS FOR EVALUATING THE PREDICTION PERFORMANCE IN COLLABORATIVE FILTERING RECOMMENDER SYSTEM

  • Lee, Seok-Jun;Lee, Hee-Choon;Chung, Young-Jun
    • Journal of applied mathematics & informatics
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    • 제28권1_2호
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    • pp.439-450
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    • 2010
  • In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

분류 속성과 Naive Bayesian을 이용한 사용자와 아이템 기반의 협력적 필터링 (User and Item based Collaborative Filtering Using Classification Property Naive Bayesian)

  • 김종훈;김용집;임기욱;이정현;정경용
    • 한국콘텐츠학회논문지
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    • 제7권11호
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    • pp.23-33
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    • 2007
  • 협력적 필터링은 피어슨 상관 계수에 의해 유사도를 구하고, 선호도를 기반으로 이웃 선정 방법을 사용하므로 아이템에 대한 내용을 반영하지 못할 뿐만 아니라 희박성 및 확장성의 문제를 가지고 있다. 이러한 문제점을 개선하기 위하여 아이템 기반 협력적 필터링이 실용화되었으나 아이템의 속성을 반영하지는 못한다. 본 논문에서는 기존 추천 시스템의 문제점을 보완하기 위하여 분류 속성과 Naive Bayesian을 이용한 사용자와 아이템 기반의 협력적 필터링을 제안하였다. 제안한 방법에서는 희박성 문제를 해결하기 위하여 명시적 데이터에 기반한 아이템 유사도와 묵시적 데이터에 기반한 사용자 유사도를 복합적으로 참조한다. 참조 결과에 대해 Naive Bayesian을 적용한다. 또한 속성을 반영하기 위해 아이템 분류속성간의 유사관계 순위를 아이템 유사도 계산에 반영함으로써 정확성을 높일 수 있었다.

현대여성(現代女性)의 의복의식(衣服意識)에 관한 조사(調査) 연구(硏究) - 서울 지역(地域)의 양복(洋服) 착용자(着用者)를 중심(中心)으로 -

  • 이희명
    • 복식
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    • 제2권
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    • pp.73-88
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    • 1978
  • This article is an attempt to explain, at least in part, the contemporary Korean women's consciousness of Western Dreasses. As time changes, the role of clothing undergoes varisous transitions, while values and ways of life are constantly in change. It is, therefore, proper and appropriate to recognize as among the major aspects of social psychology such phenomenon as interests, understanding of clothing, the choice of a dress, and attitudes toward clothing, etc. The purpose of this study is to discover problems concerning and their clothing and their solutions, by means of a surveying approach. The method of research used is based upon questionares distributed to parents of first-year pupils in elementary schools and to female clerks working in offices, covering the period from August through October, 1976. The number of the questionares distrubuted totalled 600, and 526 were returned to the research to be utilized for analysis. The contents of the survey included such things as values concerning clothing, kinds of clothing and their practical use, the selection of clothing and the method of purchase, fashions, etc. The classification of aquisition are self-made clothing, clothing made to order and ready-made materials. It is composed of 25 items, including affirmative reasons as well as negative ones. The processing of the material returned was made by using the computer, and based upon classifications such as ages, monthly income, occupations; thus diagraming the result in percentages. The conclusion made and the improvements proposed are as follows: 1. The values of clothing were placed on the expression of the wearer's personality (32.7) and on eauty(28. 6%). The lower age group places is stress upon the expression of personality, while the higher age group stresses beauty. About 50% of wearers are contented with their clothing, their clothing, the rest of whom them indicating their dissatisfaction with what they wear. As to designs at the time of selection, about 46% indicated their preference of personal expression, 31.8% on usefulness. In selecting material, practicality is emphasized; in selecting patterns, single color is preferred. In short, personal expression and esthetic values are primary, with consideration of practicality in mind. 2. The classification of clothing according to their uses indicates the highest numbers in normal wear (home wears) and clothings to be worn outside home. As to evening dresses, (party dress) only one or two articles were checked by many, and no such article was clamed to be possessed by most. The highest ratio of wearing was shown in the case of home wear (47.3%) and clothing to be worn outside the home, which is 55.8%. The budget for one article of clothing was greatest in the case of home wear, and clothing worn outside the home. Many used both kinds of articles for the same purpose. It is desirable, therefore, that the kinds of clothing should be varied according to the purpose for which they are worn, and that clothing appropriate for that purpose should be worn. 3. The motivation for purchasing clothing was highly chosen in the item of seasonal change, which was 55.7%; Clothing deliberately made was indicated by 45.2%. In the mothods of purchasing clothing, clothing made to order and ready-made was indicated by 44.4%, which is the highest; Clothing made to order was 25.4%, and self-sewing was 1.1%, which is the lowest. (1) In the case of self-sewing, "I like it but it is very hard," was checked by 43.6%; "It is so difficult that I cannot wear such clothing" was checked by 13.3%. From these, we can conclude that the questionees are willing to make clothing by themselves, but techniques involved in sewing and at her problems involved in the skill are complicated but when those problems are eliminated there is a possibility for practice. The response checked by questionees concerning the self-sewing was, "It's economical", which is a clear indication that many questionees are positive for self-sewing. It is generally believed that ready-made clothing is cheaper, but it is not necessarily so. In consideration of the quality of clothing, self-sewing is a necessity, and it is desirable that it should be encouraged. (3) Problems involved in ready-made clothing, such as designs, skills, size (fitting) should be eliminated. When these problems are scientifically gotten rid of, it is possible that affirmative returns will be expected. Affirmative responses such as "Ready-made clothing is economical," "You can select there on the spot," are good signs that many women expect to wear ready-made clothing. It is in this sense that the prospect for ready-made clothing is brighter when much development for ready-made clothing is on the way. 4. Much concern for fashion are checked in such item of questions as "Fashionable clothing in the show window," "Clothes worn by women." The first item was checked by 50.1 %, and the second was checked by 48.6%. The reason for following fashion is "Because many people wear them," which was indicated by 30.4%. The reason for not following fashion is "It is too expensive," which was checked by 29.6%. The 26.2% of the answers indicated that "Fashionable clothing is devoid of personality," The influences of fashion over the development of fashion over the development of clothing are two-fold: Esthetic and active. It is not to be deniable that people follow fashion more or less. 1978.9>

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Conditional Generative Adversarial Network(CGAN) 기반 협업 필터링 추천 시스템 (Conditional Generative Adversarial Network based Collaborative Filtering Recommendation System)

  • 강소이;신경식
    • 지능정보연구
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    • 제27권3호
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    • pp.157-173
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    • 2021
  • 소비자의 욕구와 관심에 맞추어 개인화된 제품을 추천하는 추천 시스템은 비즈니스에 필수적인 기술로서의 그 중요성이 증가하고 있다. 추천 시스템의 대표적인 모형 중 협업 필터링은 우수한 성능으로 다양한 분야에서 활용되고 있다. 그러나 협업필터링은 사용자-아이템의 선호도 정보가 충분하지 않을 경우 성능이 저하되는 희소성의 문제가 있다. 또한 실제 평점 데이터의 경우 대부분 높은 점수에 데이터가 편향되어 있어 심한 불균형을 갖는다. 불균형 데이터에 협업 필터링을 적용할 경우 편향된 클래스에 과도하게 학습되어 추천 성능이 저하된다. 이러한 문제를 해결하기 위해 많은 선행연구들이 진행되어 왔지만 추가적인 외부 데이터 또는 기존의 전통적인 오버샘플링 기법에 의존한 추천을 시도하였기에 유용성이 떨어지고 추천 성능 측면에서 한계점이 있었다. 본 연구에서는 CGAN을 기반으로 협업 필터링 구현 시 발생하는 희소성 문제를 해결함과 동시에 실제 데이터에서 발생하는 데이터 불균형을 완화하여 추천의 성능을 높이는 것을 목표로 한다. CGAN을 이용하여 비어있는 사용자-아이템 매트릭스에 실제와 흡사한 가상의 데이터를 생성하여, 희소성을 가지고 있는 기존의 매트릭스로만 학습한 것과 비교했을 때 높은 정확도가 예상된다. 이 과정에서 Condition vector y를 이용하여 소수 클래스에 대한 분포를 파악하고 그 특징을 반영하여 데이터를 생성하였다. 이후 협업 필터링을 적용하고, 하이퍼파라미터 튜닝을 통해 추천 시스템의 성능을 최대화하는데 기여하였다. 비교 대상으로는 전통적인 오버샘플링 기법인 SMOTE, BorderlineSMOTE, SVM-SMOTE, ADASYN와 GAN을 사용하였다. 결과적으로 데이터 희소성을 가지고 있는 기존의 실제 데이터뿐만 아니라 기존 오버샘플링 기법들보다 제안 모형의 추천 성능이 우수함을 확인하였으며, RMSE, MAE 평가 척도에서 가장 높은 예측 정확도를 나타낸다는 사실을 증명하였다.