基于并行化谱聚类的协同推荐算法研究

Research on collaborative recommendation algorithms based on parallel spectral clustering

随着大规模网络数据的增加,可扩展性成为推荐系统的一个关键因素,为此提出一种基于并行化谱聚类的协同推荐算法.首先通过并行化改进的谱聚类方法对项目进行聚类;然后在基于用户的协同推荐算法基础上,结合已聚类的项目打分信息,提出一种改进的相似用户计算方法,并进行推荐;最后在数据集上进行测试.结果表明,该算法可以有效降低时间复杂度,推荐精确度和推荐效率也有显著提高.

With the increase of large-scale network data, scalability has become a key factor in the recommendation system. A new collaborative recommendation algorithm is thus based on MapReduce parallel spectral clustering was proposed. First, items are clustered using the improved parallel spectral clustering method; Then, based on the user collaborative recommendation algorithm and combined with the clustered items＇ ratings, an improved calculation method for similar users is proposed to establish recommendation. The test results on the dataset show that the proposed algorithm can effectively reduce time complexity, which significantly improving its accuracy and efficiency.