A novel rating prediction method based on user relationship and natural noise
A novel rating prediction method based on user relationship and natural noise
复制标题
一种基于用户关系和自然噪声的评分预测方法
DOI：
10.1007/s11042-017-4481-8
复制
发表时间：
2018
影响因子：
3.6
通讯作者：
Long Xiang
中科院分区：
文献类型：
--
作者：
Tong Chao;Lian Yu;Niu Jianwei;Long Xiang
评分预测是推荐系统研究的一个热点。在这个领域有很多方法，如协同过滤。然而，这些方法很少考虑用户的友谊关系，这实际上包含了重要的信息，为评级预测。此外，用户评分中存在自然噪声。本文在分析用户的自然噪声和关系的基础上，提出了一种评分预测算法NF-SVM。通过对用户进行聚类，增强用户之间的相似性属性，并采用迭代算法得到用户评分质量的排序。然后，我们分析用户的评级历史，以获得用户的自然噪声的属性。所有这些属性都被用来构建SVM的训练集，以获得预测模型。我们还测试了我们的算法的数据集，这是爬下豆瓣，在中国最大的电影评级网站之一。然后，我们比较了我们的算法与其他国家的最先进的收视率预测方法。大量的实验表明，我们的算法优于其他算法。
Rating prediction is a hot spot in the research of recommender systems. There are lots of methods in this field such as collaborative filtering. However, few of these approaches take users’ friendship relationships into consideration, which actually contain significant information for rating prediction. Besides, there exists natural noise in users’ ratings. In this paper, we propose a rating prediction algorithm named NF-SVM based on the analysis of users’ natural noise and relationships. We cluster users to sharpen the similarity attribute among users, and use an iterative algorithm to obtain the rank of users’ rating quality. Then, we analyze users’ rating history to obtain the attributes of users’ natural noise. All these attributes are used to build a training set for SVM to get a prediction model. We also tested our algorithm in a data set which is crawled down from Douban, one of the largest movie rating web sites in China. Then we compared our algorithm with other state-of-the-art rating prediction methods. Extensive experiments show that our algorithm outperforms the other algorithms.