一种结合项目属性的混合推荐算法

传统的协同过滤推荐算法中仅仅根据评分矩阵进行推荐,由于矩阵的稀疏性,存在推荐质量不高的问题.本文提出了一种结合项目属性相似性的混合推荐算法,该算法通过计算项目之间属性的相似性,并且与基于项目的协同过滤算法中的相似性动态结合,通过加权因子的变化控制两种相似性的比重来改善协同过滤中的稀疏性问题,并且将综合预测评分和基于用户的协同过滤预测评分相结合来提高推荐质量,最终根据综合评分来进行推荐.通过实验数据实验证明,该算法解决了协同过滤算法的矩阵稀疏性问题.

Hybrid Recommendation Algorithm Combined with the Project Properties

Traditional collaborative filtering recommendation algorithm only bases on matrix.Due to the sparsity of matrix, the quality of recommendation is not high.This paper proposes a hybrid recommendation algorithm whose similarity is combined with the properties of projects.This algorithm improves the data sparseness in collaborative filtering through the change of the weighted factor, controlling the proportion of two kinds of similarity that one is the similarity of attribute between projects and the other is the similarity of item-based collaborative filtering algorithm.And the comprehensive prediction score and user-based collaborative filtering prediction score are combined to improve the quality of recommendation.Finally, the recommendation is given according to the comprehensive scores.Experiments show that the algorithm has better recommendation quality.