期刊
出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2019.01.258
关键词
recommendation system; User-based Collaborative filtering; similarity measure; versatility problem; sparse data
资金
- National Key R&D Program of China [2017YFC0806205]
In the field of recommendation system, the memory-based Collaborative filtering has been proven to be useful in lots of practices. Similarity measures like Pearson correlation coefficient tend to only focus on improving as much as possible the accuracy. Handling datasets with different features, exiting measures cannot apply to different types of data simultaneously. In this paper, an improved similarity measure Common Pearson Correlation Coefficient (COPC) was proposed. Unlike existing measures, it strongly depends on chosen distance function, which adhere to the natural property of monotonicity and utilize consensus evaluation measure to capture an optimal value to improve PCC measure. To mitigate sparse problem, we also introduce the Hellinger Distance (Hg) as global similarity to lower the impact of lacking co-rated items. Experimental results on real-world datasets demonstrates that our measure outperformed the existing schemes of predicting ratings. (C) 2019 The Authors. Published by Elsevier B.V.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据