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- W2785957819 abstract "In this paper, we propose an approach of course recommender system for the subject of information management speciality in China. We collect the data relative to the course enrollment for specific set of students. The sparse linear method (SLIM) is introduced in our approach to generate the top-N recommendations of courses for students. Furthermore, the L 0 regularization terms were presented in our proposed optimization method based on the observation of the entries in recommendation system matrix. Expert knowledge based comparing experiments between state-of-the-art methods and our method are conducted to evaluate the performance of our method. Experimental results show that our proposed method outperforms state-of-the-art methods both in accuracy and efficiency." @default.
- W2785957819 created "2018-02-23" @default.
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- W2785957819 date "2018-01-01" @default.
- W2785957819 modified "2023-09-27" @default.
- W2785957819 title "Sparse Linear Method Based Top-N Course Recommendation System with Expert Knowledge and L 0 Regularization" @default.
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- W2785957819 doi "https://doi.org/10.1007/978-3-319-74521-3_15" @default.
- W2785957819 hasPublicationYear "2018" @default.
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