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Discovery of Preference through Learning Profile for Content-based Filtering

내용 기반 필터링을 위한 프로파일 학습에 의한 선호도 발견

  • Published : 2008.02.28

Abstract

The information system in which users can utilize to control and to get the filtered information efficiently has appeared. Content-based filtering can reflect content information, and it provides recommendation by comparing the feature information about item and the profile of preference. This has the shortcoming of the varying accuracy of prediction depending on teaming method. This paper suggests the discovery of preference through learning the profile for the content-based filtering. This study improves the accuracy of recommendation through learning the profile according to granting the preference of 6 levels to estimated value in order to solve the problem. Finally, to evaluate the performance of the proposed method, this study applies to MovieLens dataset, and it is compared with the performance of previous studies.

Keywords

Recommender System;Information Retrieval;Data Mining;Content Based Filtering

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  2. Applying Rating Score's Reliability of Customers to Enhance Prediction Accuracy in Recommender System vol.13, pp.7, 2013, https://doi.org/10.5392/JKCA.2013.13.07.379