融合项目标签相似性的协同过滤推荐算法

摘要

针对传统推荐算法在相似性计算和评分预测方法中存在预测精度和稳定性的不足,为进一步提高算法精确度和稳定性,提出一种新的推荐算法.首先,依据各项目的重要标签的数量,计算出项目间M2相似性,依据该相似性构成该项目的邻近项目集;然后,参考Slope One加权算法思想,定义了新的评分预测方法;最后,使用该评分方法基于邻近项目集对用户评分进行预测.为了验证该算法的准确性和稳定性,在MovieLens数据集上与基于曼哈顿距离的K-最近邻(KNN)算法等传统推荐算法进行了对比,实验结果表明该算法与KNN算法相比平均绝对误差下降7.6％,均方根误差下降7.1％,并且在稳定性方面也更好,能更准确地为用户提供个性化推荐.%Aiming at the shortages in similarity calculation and rating prediction in traditional recommendation system,in order to further improve the accuracy and stability of the algorithm,a new recommendation algorithm was proposed.Firstly,according to the number of important labels for an item,the M2 similarity between the item and other items was calculated,which was used to constitute the nearest item set of the item.Then,according to the Slope One weighting theory,a new rating prediction method was designed to predict users' ratings based on the nearest item set.To validate the accuracy and stability of the proposed algorithm,comparison experiments with the traditional recommendation algorithms including K-Nearest Neighbor (KNN) algorithm based on Manhattan distance were conducted on MovieLens dataset.The experimental results showed that compared with the KNN algorithm,the mean absolute error and the root mean square error of the new algorithm were decreased by 7.6％ and 7.1％ respectively.Besides,the proposed algorithm performs better in stability,which can provide more accurate and personalized recommendation.