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• W2026448017 abstract "오늘날 컴퓨팅 환경의 진보와 웹의 이용이 활발해짐에 따라 오프라인에서 이루어졌던 있었던 많은 서비스들과 상품의 제공이 웹에서 이루어지고 있다. 이러한 웹 기반 서비스 및 상품은 개인에 적합하게 취사선택되어 제공되는 추세이다. 이렇듯 개인에 적합한 서비스 및 상품의 선택과 제공을 위한 패러다임을 개인화(personalization)라 한다. 개인화된 서비스 및 상품의 제공을 위한 분야로서 연구된 것이 추천(recommendation)이다. 그러나 이러한 추천 기법들은 신규 사용자에게 적합한 추천을 제공하지 못하는 문제와 사용자의 상품에 대한 평점에만 의존하여 추천을 생성한다는 계산 공간에서의 제약 사항을 가지고 있다. 두 문제 모두 추천 분야에서 지속적인 관심을 보이는 분야로서 신규사용자 추천 문제의 경우는 신규 사용자의 평점이 없기 때문에 유사 사용자들을 분류할 수 없음에 기인한다. 그리고 추천 공간 제약에 따른 문제는 추천 차원의 추가에 따른 처리 비용이 급격히 증가한다는 문제를 가지고 있기 때문에 쉽게 접근하기 어렵다. 따라서 본 논문에서는 신규사용자 추천 향상을 위한 기법과 평점 예측 시 예측에 대한 가중치를 적용하는 기법을 제안한다. Today, many services and products that used to be only provided on offline have been being provided on the web according to the improvement of computing environment and the activation of web usage. These web-based services and products tend to be provided to customer by customer's preferences. This paradigm that considers customer's opinions and features in selecting is called personalization. The related research field is a recommendation. And this recommendation is performed by recommender system. Generally the recommendation is made from the preferences and tastes of customers. And recommender system provides this recommendation to user. However, the recommendation techniques have a couple of problems; they do not provide suitable recommendation to new users and also are limited to computing space that they generate recommendations which is dependent on ratings of products by users. Those problems has gathered some continuous interest from the recommendation field. In the case of new users, so similar users can't be classified because in the case of new users there is no rating created by new users. The problem of the limitation of the recommendation space is not easy to access because it is related to moneywise that the cost will be increasing rapidly when there is an addition to the dimension of recommendation. Therefore, I propose the solution of the recommendation problem of new user and the usage of item quality as weight to improve the accuracy of recommendation in this paper." @default.
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• W2026448017 date "2009-04-30" @default.
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• W2026448017 title "Weight Based Technique For Improvement Of New User Recommendation Performance" @default.
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