Fuzzy Collaborative Filtering for E-learning CourseRecommendationFuzzy Collaborative Filtering for E-learning Course Recommendation

• Main Authors: SHUE-HUEI TSAI, 蔡淑慧
• Other Authors: Chein-Shung Hwang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/84454099497220995424

碩士 === 中國文化大學 === 資訊管理研究所碩士在職專班 === 94 === E-learning on the Web has attracted much attention recently. Many of current systems focus on analyzing the learners’ behaviors from the web logs and provide course recommendations for them. One of the most successful and widely used approaches is collaborative filtering (CF). CF approach finds other learners that have shown similar tastes to the current learner and recommends what they have liked to that learner. The likeness can be measured by the viewing time that learns spent on a specific course unit. In this paper, we propose a course recommender system that combines collaborative filtering and fuzzy set. It is designed to better address the sharp boundary problem of discretizing the viewing time. The proposed model contains three modules: preprocessing, unit inference and unit prediction. Preprocessing module includes data cleaning and fuzzy value computation of each unit. Unit inference computes the similarity between each pair of units based on their unit profiles. Unit prediction provides a recommendation list of unseen units for the current leaner. We use the hit-ratio metric and click-soon-ratio metric to evaluate the quality of a recommendation. The experimental results show that the proposed methods can achieve a better performance than the Traditional CF without time information.