融合专家信任的协同过滤推荐算法

针对目前协同过滤推荐算法存在的数据稀疏性和冷启动等问题,对融合专家信任的协同过滤推荐算法进行了研究和改进.改进算法结合DBSCAN初始聚类中心优化的思想,将用户划分到不同的社区簇中.考虑到用户活跃度偏差对相似度计算的影响,加入用户活跃度惩罚权重对相似度进行了改进.在选取了专家用户后,考虑到专家评估过的不同项目的专家信任度值不是一成不变的,引入项目平衡因子来处理项目之间的差异,使专家对其评价过的每个项目都有独立的专家信任度值.MovieLens数据集上的实验结果显示,该算法可有效缓解数据稀疏性及冷启动问题,提高了推荐精度.

Collaborative Filtering Recommendation Algorithm Mix Together with Expert Trust

This study proposes an improved collaborative filtering recommendation algorithm integrating expert trust aiming at the data sparsity and cold start in the current algorithms. This algorithm divides users into different community clusters based on the optimization of initial clustering centers in DBSCAN. Considering the influence of user activity on similarity calculation, we introduce the penalty weight of user activity to improve the similarity calculation. After expert selection, the balance factors in projects are introduced, since the expert trust for different projects varies. Thus, each project evaluated has an independent expert trust. Experimental results on the MovieLens data set show that the proposed algorithm can effectively alleviate data sparsity and cold start, increasing the recommendation accuracy.