基于改进最近邻的协同过滤推荐算法

Collaborative filtering algorithm based on improved nearest neighbors

针对当前协同过滤推荐算法易受数据稀疏性与冷启动的问题,提出了一种改进最近邻的协同过滤推荐算法。建立用户-项目评分矩阵,并度量项目之间、用户之间的相似性,获取项目和用户的最近邻居,其中最近邻居的最优参数k值采用粒子群算法选择,在Movie Lens和Book-Crossing数据集上进行了仿真对比实验。结果表明,相对于其他协同过滤推荐算法,该算法降低了平均绝对误差值,提升了推荐准确度,达到提高推荐质量效果的目的。

Aiming to the problems that the quality and precision are caused by the sparse user scorings and cold-start, a novel collaborative filtering algorithm based on improved nearest neighbors is proposed in this paper. User-item matrix is established, and similarity between items and users is measured, the nearest neighbor of items and users is acquired, in which the particle swarm optimization algorithm is used to select the optimal value of the parameter k, the simulation experiments are carried out on Movie Lens and Book-Crossing dataset. The results show that the proposed algorithm can achieve lower MAE and efficiently improve recommendation precision, and it can enhance the quality of recommendations.