结合用户属性聚类的协同过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on User Attributes Clustering

协同过滤算法利用大量数据,通过研究用户的喜好可以为用户推荐其感兴趣的项目,在电子商务得到了广泛应用。然而,此类算法在面临扩展性、数据稀疏性和冷启动等问题时,出现推荐准确度下降和推荐效率偏低的问题。针对这些问题,本文引入用户属性相似度的概念,使用K-means聚类算法将用户划分到恰当用户簇,预测用户对项目的评分。然后,通过混合加权的方法,将基于用户属性的K均值聚类的推荐算法与基于项目的协同过滤算法相融合,提出综合用户属性的协同过滤算法。通过在Movie Lens数据集上进行实验,结果表明本文所提出的算法具有可扩展性,同时在一定程度上缓解了冷启动问题,提高了推荐算法的预测准确度。

Collaborative filtering algorithm , which can recommend the items appeal to users from mass of data through studying the user ’s preferences is widely used in electronic commerce .However , collaborative filtering algorithm suffers from decreasing accuracy and inefficiency in scalability , data sparsity , and cold start .In order to solve there problems , the concept of user attrib-ute similarity is introduced in this paper , and the user can be divided into appropriate user clusters to predict the user ’ s ratings for a project by using K-means clustering algorithm .Furthermore, through fusing the recommendation algorithm based on user at-tributes and the collaborative filtering algorithms based on the project by using the method of mixed weights , a collaborative filte-ring algorithm synthesizing the attributes of user is proposed .Through experiment by using MovieLens data sets , we verify that the proposed algorithm has extensibility .Simultaneously , it can ease cold start problem and improve the prediction accuracy of recom-mendation algorithm in some degree .