一种缓解协同过滤算法数据稀疏性的方法

摘要

The Collaborative Filtering Algorithm is one of the most ancient algorithm in recommendation system.At the same time,with the simpilicity and high efficiency,the collaborative filtering algorithm has encountered data spar-sity and cold starts problems.In this article,against the issue in data sparsity,we propose a method based on interes-tingness to predict the unrated scores by users.At the end of the ariticle,we made experiments based on Netflix dataset,and indicates that the method could better process the sparse matrix,and relieve the issue in data sparsity,and enhence improve the accuracy of collaborative filtering algorithm.%协同过滤算法是推荐系统中最古老的算法之一,同时也是当今推荐系统中使用最广泛的一种算法.但是在简单,效率高的同时,协同过滤算法还存在数据稀疏性,冷启动等一些问题.本文针对其数据稀疏性的问题,提出了一种根据兴趣度预测用户未评分项目的方法.最后通过基于Netflix数据集的实验结果表明,该方法能够更好的处理稀疏矩阵,能缓解数据稀疏问题,从而提高了协同过滤算法的准确性.