基于随机游走和聚类平滑的协同过滤推荐算法

A Collaborative Filtering Algorithm Based on Random Walk and Cluster-based Smoothing

协同过滤是电子商务推荐系统中被广泛采用的技术,然而数据稀疏性会影响协同过滤的推荐质量。本文针对数据稀疏问题提出一种基于随机游走和聚类平滑的两阶段协同过滤推荐算法。离线阶段:计算项目间相关性,提出了一个新的方法即通过加权累加各步转移概率对项目间相关性进行描述。根据得到的项目相关性矩阵对项目聚类,利用聚类信息对未评分数据进行平滑处理。在线阶段:根据离线阶段得到的项目间相关性查找目标项目的邻居并进行预测。本文提出的方法能加强项目间相关性的描述。实验表明,根据用该方法得到的项目相关性矩阵查找邻居更加准确,可以有效地缓解稀疏数据的影响,改善推荐的性能。

Collaborative filtering has been widely used in E-Commerce recommendation systems,but the sparsity of data affects the quality of collaborative filtering recommendation.A two-stage collaborative filtering algorithm is proposed based on random walking and cluster-based smoothing.For off-line stage,calculate the correlation between items,suggest a new method which describes the correlation between items by cumulating weighted transition probability of each step.Cluster items according to the item correlation matrix,then smooth the unrated data by using clustering information.For online stage,search the target item＇s neighbors according to the correlation between items cumulated during the off-line and predict.This method can enhance the description of the correlation between items.The experiment results illustrate that searching neighbors according to the item correlation matrix will become more accurate,which can effectively relieve the impact of sparse data and improve the quality of recommendation.