On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy
On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy
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DOI：
10.1007/978-3-030-26773-5_17
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Y. Kanzawa
中科院分区：
文献类型：
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作者：
Y. Kanzawa
提出了一种基于Tsallis熵的球形数据可能性聚类协同过滤方法。这项研究的动机是由以前的工作，这表明，采用模糊聚类的球形数据CF任务提供了更好的推荐精度比模糊聚类的分类多变量数据。此外，可能性聚类算法自然比模糊聚类对噪声更鲁棒。在人工数据集和真实的数据集上进行的实验结果表明，该方法在推荐准确率方面优于传统方法。
This paper proposes a collaborative filtering (CF) method using possibilistic clustering for spherical data based on Tsallis entropy. This study was motivated by a previous work, which showed that adopting fuzzy clustering for spherical data in CF tasks provided better recommendation accuracy than fuzzy clustering for categorical-multivariate data. Moreover, possibilistic clustering algorithms are naturally more robust to noise than fuzzy clustering. The results of experiments conducted on an artificial dataset and one real dataset indicate that the proposed method is better than the conventional methods in terms of recommendation accuracy.