基于协同过滤的三支粒推荐算法研究

Three-way Granular Recommendation Algorithm Based on Collaborative Filtering

为了降低传统协同过滤算法的推荐成本,并解决该算法评分信息单一的问题,提出了一种基于协同过滤的三支粒推荐算法。该算法在传统协同过滤的基础上,考虑项目特征对用户评分的影响,根据项目特征、粒化用户项目评分矩阵,形成用户对项目粒度的评分矩阵,并以此作为用户偏好的测度依据。同时,该算法在推荐过程中引入三支决策,考虑了推荐过程中产生的误分类成本和学习成本,并基于用户真实的评分偏好构建三支推荐。实验结果显示,基于协同过滤的三支粒推荐算法与传统协同过滤算法相比,不但提高了算法的推荐质量,而且降低了推荐成本。

To decrease the recommendation cost and solve the problem of single rating of traditional collaborative filtering algorithm,this paper proposed a three-way granular recommendation algorithm based on collaborative filtering.On the basis of collaborative filtering,this algorithm considers the influence of items' characteristics on users' ratings,and constructs user-item's granulation rating matrix through granulating user-item rating matrix by the characteristics of items,which is used to measure users' preferences.At the same time,this algorithm considers both misclassification cost and teacher cost during the process of recommendation,and constructs three-way recommendation based on users' real preferences on rating.Experimental results show that compared with traditional collaborative filtering algorithm,three-way granular recommendation algorithm based on collaborative filtering not only improves the quality of the recommendation,but also decreases the recommendation cost.