This paper has developed an online personalized English learning recommender system capable of providing ESL students with reading lessons that suit their different interests and therefore increase the motivation to learn. The system, using content-based analysis, collaborative filtering, and data mining techniques, analyzes real students' reading data and generates recommender scores, based on which to help select appropriate lessons for respective students. Its performance having been tracked over a period of one year, this recommender system has proved to be very useful in heightening ESL learners' motivation and interest in reading.
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