本文選題：社交網(wǎng)絡 + 協(xié)同過濾　； 參考：《現(xiàn)代教育技術(shù)》2016年02期

【摘要】：傳統(tǒng)的協(xié)同過濾推薦算法存在冷啟動和數(shù)據(jù)稀疏的問題,使得新學習者因歷史學習行為記錄稀疏或缺失而無法獲得較準確的個性化學習資源推薦。鑒于此,文章提出將學習者社交網(wǎng)絡信息與傳統(tǒng)協(xié)同過濾相融合的方法,計算新學習者與好友之間的信任度,借助新學習者好友對學習資源的評分數(shù)據(jù),來預測新學習者對學習資源的評分值,以填補新學習者在學習者—學習資源評分矩陣中的缺失,實現(xiàn)對新學習者的個性化學習資源推薦。實證研究結(jié)果表明,該方法在一定程度上能夠解決傳統(tǒng)協(xié)同過濾方法的冷啟動和數(shù)據(jù)稀疏問題,提高個性化學習資源推薦的準確率。
[Abstract]:The problems of cold start and sparse data in the traditional collaborative filtering recommendation algorithm make it impossible for new learners to obtain more accurate personalized learning resources recommendation due to sparse or missing history learning behavior records. In view of this, this paper proposes a method that combines the information of learners' social networks with traditional collaborative filtering, calculates the trust between new learners and their friends, and makes use of the new learners' friends' scoring data for learning resources. To predict the new learners' scores on learning resources, to fill the gaps in the Learner-Learner Resource scoring Matrix, and to realize the personalized learning resources recommendation for the new learners. The empirical results show that this method can solve the cold start and data sparse problems of traditional collaborative filtering methods to some extent and improve the accuracy of personalized learning resources recommendation.
【作者單位】： 湖北大學教育學院;
【基金】：教育部人文社會科學研究青年基金項目“基于互動電視的課堂教學模式與策略研究”(項目編號:14YJC880109)階段性研究成果
【分類號】：G434
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本文編號：2047796