摘要
协同过滤算法是个性化推荐中应用最成功的技术之一,计算用户间的相似性是协同过滤算法的关键。而传统的相似性度量方法在数据稀疏和小用户交集时性能严重下降,因此本文提出了一种基于改进信息熵的相似性度量方法(NWDE),充分考虑了数据稀疏环境的特点,在计算用户间的相似性时综合考虑了用户间的交集大小以及评分差异的大小,使其得分更加真实。实验结果表明,在数据稀疏和小用户交集的情况下,该算法的推荐精度比传统方案取得了显著的改善。
Similarity measurement is the most crucial component to determine recommendation accuracy in memory-based collaborative filtering algorithms. Most existing calculations of similarities suffer from data sparsity and poor prediction quality problems. This paper proposed a novel similarity measures based on improved entropy （NWDE）, which consider the features of data sparsity. The entropy is computed by the difference of two users＇ ratings, and we also consider the size of their common rated items, the size is bigger, the weight of their similarity is higher. The algori＇thm 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.
出处
《微计算机信息》
2012年第8期181-183,共3页
Control & Automation
关键词
协同过滤
相似性度量
信息熵
个性化推荐
Collaborative Filtering
Similarity Measures
Entropy
Recommendation Systems
