基于加权两层图的混合推荐方法

Hybrid Recommendation Filtering Method Based on Weighted Two-layer Graph

结合用户-项目评分矩阵和项目-类别关联矩阵,提出了一种新的混合推荐模型。首先,利用用户-项目评分矩阵和项目-类别矩阵,提出一种新的项目关联度度量方法,该方法根据项目的特征信息和当前评分数据的稀疏情况,动态调节关联度的计算值,真实地反映彼此之间的关联度;其次,分别以项目关联度和用户-项目评分信息为权值,构建一个基于用户-项目的加权两层图模型;在此基础上,从两层图的全局结构出发,结合随机游走算法给出了基于加权两层图的推荐算法,以为用户提供个性化的项目推荐和用户推荐。实验结果表明,该算法相比文献中的其他推荐方法具有更高的准确度。

Combined with the rating matrix of user-item and the correlation matrix of item-category,a new hybrid recommended model was proposed.First,a new correlation degree measuring algorithm was presented by using these two matrixes.This algorithm takes into account the feature information and dynamically adjusts the result based on the sparse situation of the rating data,truly reflects the degree of association with each other.Then,a new weighted two-layer graph model was constructed by using the item-item correlation degree and the user-item correlation degree as the weight.On this basis,starting from the global structure of the two-layer graph,the recommendation algorithm based on weighted two-layer graph was given by the random walk algorithm,to provide users with personalized item recommendations and user recommendations.The experiments show that the algorithm compared to other recommended models in the references has higher accuracy.