一种基于用户行为相似度的协同推荐算法
首发时间:2010-09-06
摘要:如何计算用户之间的相似度是协同推荐算法中最关键的技术,而现有算法在数据稀疏或小邻居集的环境下性能严重下降。本文提出了一种基于用户行为相似度的协同推荐算法(UBS),它充分考虑数据稀疏环境的特点,从评分矩阵中挖掘用户的网络行为特征,得到用户之间更真实的用户行为相似度,最终有效地解决了传统方案在稀疏数据和小邻居集下的相似度不准确问题。实验结果表明该算法在稀疏数据和小邻居集环境下,推荐质量都取得了明显的改进效果。
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Collaborative Filtering based on User Behavior Similarity
Abstract:The most crucial component of the algorithm is the mechanism of calculating similarities among users. Most existing calculations of similarities suffer from data sparsity and poor prediction quality problems. In this paper, a new CF algorithm based on User Behavior Similarity (UBS) is proposed, which extracts users' network behaviors' characteristics from the sparse rating matrix based on fully consideration of the features of data sparsity. By introducing a more realistic similarity measure, which is named user behavior similarity, the algorithm effectively solves the problem of the inaccuracy of similarities in data sparsity or small size neighborhood environments. Experiments show that algorithm outperforms other state-of-the-art CF algorithms and it is more robust against data sparsity.
Keywords: collaborative filtering data sparsity user behavior similarity neighborhood set
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