余弦相似度加权的Slope One协同过滤算法研究  

Research of Slope One Cooperative Filtering Algorithm Based on Cosine Similarity Weighting

作　　者：周化[1] 袁志[1] 

机构地区：[1]广州大学华软软件学院网络技术系,广东广州

出　　处：《计算机科学与应用》2017年第10期1036-1044,共9页Computer Science and Application

基　　金：湖南省自然科学基金(2016JJ4090);广东省攀登计划基金(PDJH2016a0991)。

摘　　要：针对slope one协同过滤算法中存在的数据稀疏性问题展开研究。提出一种基于余弦相似度加权的协同过滤算法(COSLOPE算法)。用加权slope one算法填充稀疏的评分矩阵后利用cosine算法计算用户之间的相似度,得出目标用户的近邻矩阵。通过近邻矩阵中拥有评分记录的用户来预测目标用户的项目评分,并进行推荐。该算法通过MovieLens数据集验证,MAE、RMSE 和MSE的值均优于传统Slope One算法。COSLOPE算法在有效解决数据稀疏性的同时亦提高了传统推荐算法的准确度并降低了算法响应时间。In this paper, we propose a collaborative filtering algorithm based on cosine similarity weight (COSLOPE algorithm). The similarity between the users is calculated by the cosine algorithm;the weights are determined according to the similarity degree and the scoring matrix is filled in order to establish the nearest neighbor set with high similarity to the object user. The nearest neighbor set of the nearest neighbor set is to predict the target user’s project grade and make recommendations. The algorithm is validated by the MovieLens dataset, and the values of MAE, RMSE and MSE are superior to the traditional Slope One algorithm. COSLOPE algorithm is not only in the effective solution of data sparseness, but also improve the accuracy of the traditional recommendation algorithm and reduce the algorithm response time.

关 键 词：余弦相似度 SLOPE One算法 数据稀疏性 协同过滤 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]