基于学习情况协同过滤算法的个性化学习推荐模型研究*
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苏庆,陈思兆,吴伟民,李小妹,黄佃宽
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Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation
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Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan
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表5 应用不同推荐模型的指标值
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Table 5 Indicator Values of Recommendation Models
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TOP-N | 推荐模型 | precision | recall | F | 5 | Pearson-CF | 0.609 4 | 0.551 4 | 0.579 0 | Edurank | 0.630 6 | 0.578 5 | 0.603 4 | CF-SPM | 0.653 9 | 0.599 7 | 0.625 6 | LS-PLRM | 0.679 4 | 0.616 4 | 0.646 4 | 10 | Pearson-CF | 0.623 5 | 0.564 7 | 0.592 6 | Edurank | 0.657 5 | 0.583 5 | 0.618 3 | CF-SPM | 0.696 5 | 0.606 5 | 0.648 4 | LS-PLRM | 0.730 5 | 0.614 9 | 0.667 7 | 15 | Pearson-CF | 0.644 6 | 0.574 1 | 0.607 3 | Edurank | 0.682 1 | 0.593 1 | 0.634 5 | CF-SPM | 0.717 0 | 0.605 0 | 0.656 3 | LS-PLRM | 0.728 7 | 0.621 6 | 0.670 9 | 20 | Pearson-CF | 0.654 7 | 0.585 2 | 0.618 0 | Edurank | 0.716 6 | 0.600 8 | 0.653 6 | CF-SPM | 0.717 8 | 0.611 9 | 0.660 6 | LS-PLRM | 0.737 9 | 0.631 2 | 0.680 4 |
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