基于影响集的协作过滤推荐算法

传统的基于用户的协作过滤推荐系统由于使用了基于内存的最近邻查询算法,因此表现出可扩展性差、缺乏稳定性的缺点.针对可扩展性的问题,提出的基于项目的协作过滤算法,仍然不能解决数据稀疏带来的推荐质量下降的问题(稳定性差).从影响集的概念中得到启发,提出一种新的基于项目的协作过滤推荐算法CFBIS(collaborative filtering based on influence sets),利用当前对象的影响集来提高该资源的评价密度,并为这种新的推荐机制定义了计算预测评分的方法.实验结果表明,该算法相对于传统的只基于最近邻产生推荐的项目协作过滤算法而言,可有效缓解由数据集稀疏带来的问题,显著提高推荐系统的推荐质量.

A Collaborative Filtering Recommendation Algorithm Based on Influence Sets

The traditional user-based collaborative filtering (CF) algorithms often suffer from two important problems: Scalability and sparsity because of its memory-based k nearest neighbor query algorithm. Item-Based CF algorithms have been designed to deal with the scalability problems associated with user-based CF approaches without sacrificing recommendation or prediction accuracy. However, item-based CF algorithms still suffer from the data sparsity problems. This paper presents a CF recommendation algorithm, named CFBIS (collaborative filtering based on influence sets), which is based on the concept of influence set and is a hot topic in information retrieval system. Moreover, it defines a new prediction computation method for this new recommendation mechanism. Experimental results show that the algorithm can achieve better prediction accuracy than traditional item-based CF algorithms. Furthermore, the algorithm can alleviate the dataset sparsity problem.