In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of consider...In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.展开更多
为了解决过度稀疏的评级矩阵导致矩阵分解中的过拟合问题,提出了一种融合标签和时间信息的矩阵分解推荐模型TTMF(matrix factorization recommendation algorithm fusing tags and time information),以丰富单一数据源,缓解矩阵分解中...为了解决过度稀疏的评级矩阵导致矩阵分解中的过拟合问题,提出了一种融合标签和时间信息的矩阵分解推荐模型TTMF(matrix factorization recommendation algorithm fusing tags and time information),以丰富单一数据源,缓解矩阵分解中的过拟合问题.首先通过评级数据和标签信息定义用户标签偏好值和项目标签关联度,分别表征用户对标签的兴趣、标签信息和项目之间的联系,并增加时间信息表示用户兴趣随时间的变化;然后,建立用户—项目、用户—标签和项目—标签矩阵模型,通过梯度下降法进行矩阵分解,完成推荐.基于MovieLens数据集实验结果显示,TTMF算法的RMSE(root mean square error)比传统方法LFM(latent factor model)降低了7%.TTMF算法具有更好的推荐效果.展开更多
MOOC(慕课,Massive Open Online Course)大规模在线开放课程是一个在线课堂,旨在通过网络让用户随时无限制学习。为了让用户发现感兴趣且高质量的课程,文中设计了一种基于协同过滤技术的MOOC个性化课程推荐系统,为用户提供个性化的课程...MOOC(慕课,Massive Open Online Course)大规模在线开放课程是一个在线课堂,旨在通过网络让用户随时无限制学习。为了让用户发现感兴趣且高质量的课程,文中设计了一种基于协同过滤技术的MOOC个性化课程推荐系统,为用户提供个性化的课程选择,以提升用户体验。同时对协同过滤算法存在的缺陷提出了改进思路,并对推荐技术以及MOOC未来的发展进行了展望。展开更多
基金Supported by the National Natural Science Foun-dation of China (60573095)
文摘In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.
文摘为了解决过度稀疏的评级矩阵导致矩阵分解中的过拟合问题,提出了一种融合标签和时间信息的矩阵分解推荐模型TTMF(matrix factorization recommendation algorithm fusing tags and time information),以丰富单一数据源,缓解矩阵分解中的过拟合问题.首先通过评级数据和标签信息定义用户标签偏好值和项目标签关联度,分别表征用户对标签的兴趣、标签信息和项目之间的联系,并增加时间信息表示用户兴趣随时间的变化;然后,建立用户—项目、用户—标签和项目—标签矩阵模型,通过梯度下降法进行矩阵分解,完成推荐.基于MovieLens数据集实验结果显示,TTMF算法的RMSE(root mean square error)比传统方法LFM(latent factor model)降低了7%.TTMF算法具有更好的推荐效果.
文摘MOOC(慕课,Massive Open Online Course)大规模在线开放课程是一个在线课堂,旨在通过网络让用户随时无限制学习。为了让用户发现感兴趣且高质量的课程,文中设计了一种基于协同过滤技术的MOOC个性化课程推荐系统,为用户提供个性化的课程选择,以提升用户体验。同时对协同过滤算法存在的缺陷提出了改进思路,并对推荐技术以及MOOC未来的发展进行了展望。
