一种改进的协同过滤推荐算法

推荐系统在电子商务中应用广泛，协同过滤是推荐系统中应用最为成功的推荐技术之一。随着电子商务系统数据不断增加，用户-项目评分矩阵稀疏性问题日趋明显，成为推荐系统的瓶颈。本文提出基于LDA的协同过滤改进算法，提升稀疏评分矩阵下的推荐质量。首先，根据用户与项目评分矩阵，建立LDA模型，得到用户-项目概率矩阵，作为协同过滤的原始数据；然后根据属性对项目聚类，对用户-项目概率矩阵进行裁剪；最后，考虑上下文信息，在传统协同过滤相似度计算基础上，通过引入时间因子函数改进相似度计算公式。在Movie Lens数据集上的实验结果表明，本文提出模型的MAE指标优于传统协同过滤算法。

An Improved Collaborative Filtering Recommendation Algorithm

Recommendation system is widely used in e-commerce, and collaborative filtering is one of the most successful techniques in the recommendation system. With the increasing of the e-commerce data, the problem of the sparsity of the user-item rating matrix becomes more and more obvious, which has become the bottleneck of the recommendation system. To improve the recommendation quality under the sparse dataset environment, this paper proposed an improved collaborative filtering algorithm based on LDA model. We first built LDA model according to the user-item rating matrix, and got user-item selection probability matrix. And then, we clustered the item set by item properties, and cut the matrix by cluster results. Finally, in the process of similarity calculation, we introduced time factor to improve similarity calculation formula. Experimental results on Movie Lens datasets show that the proposed model gets better performance than traditional collaborative filtering algorithm in MAE.