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A similarity measure based on Kullback-Leibler divergence for collaborative filtering in sparse data

机译:基于Kullback-Leibler散度的稀疏数据协同过滤相似度度量

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摘要

In the neighbourhood-based collaborative filtering (CF) algorithms, a user similarity measure is used to find other users similar to an active user. Most of the existing user similarity measures rely on the co-rated items. However, there are not enough co-rated items in sparse dataset, which usually leads to poor prediction. In this article, a new similarity scheme is proposed, which breaks free of the constraint of the co-rated items. Moreover, an item similarity measure based on the Kullback-Leibler (KL) divergence is presented, which identifies the relation between items based on the probability density distribution of ratings. Since the item similarity based on KL divergence makes full use of all ratings, it owns better flexibility for sparse datasets. The CF algorithm using our proposed similarity scheme is implemented and compared with some classic CF algorithms. The compared results show that the CF using our similarity has better predictive performance. Therefore, our similarity scheme is a good solution for the sparsity problem and has great potential to be applied to recommendation systems.
机译:在基于邻居的协作过滤(CF)算法中,用户相似性度量用于查找与活动用户相似的其他用户。现有的大多数用户相似性度量标准都依赖于共同评估的项目。但是,稀疏数据集中没有足够的co-rated项目,这通常会导致较差的预测。在本文中,提出了一种新的相似性方案,该方案摆脱了共同评分项目的约束。此外,提出了一种基于Kullback-Leibler(KL)散度的项目相似性度量,该度量基于等级的概率密度分布识别项目之间的关系。由于基于KL散度的项目相似度充分利用了所有评分,因此对于稀疏数据集具有更好的灵活性。使用我们提出的相似性方案的CF算法得以实现,并与一些经典CF算法进行了比较。比较结果表明,使用我们的相似性的CF具有更好的预测性能。因此,我们的相似性方案是稀疏性问题的一个很好的解决方案,具有很大的潜力可应用于推荐系统。

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