基于条件型游走二部图协同过滤算法

Collaborative filtering algorithm based on conditional walk bipartite graph

针对拥有少量评分的新用户采用传统方法很难找到目标用户的最近邻居集的问题,提出了一种条件型游走二部图协同过滤算法。首先根据复杂网络理论的二部图网络,将用户—项目评分矩阵转换为用户—项目二部图,采用条件型游走计算目标用户与其他用户之间的相似性;然后根据协同过滤算法预测未评分项目,产生推荐。研究结果表明,在同样的数据稀疏性情况下,基于条件型游走二部图协同过滤算法在MAE和准确率都要优于其他两种传统的协同过滤算法,从而提高了算法的推荐精度;而且当训练值的比例很低时,即数据稀疏程度越大时,算法推荐质量的提高程度越大。

For new users with a small number of rating,it is difficult to find the approximate neighbor set of the target user by the traditional method. This paper proposed a collaborative filtering algorithm based on conditional walk bipartite graph. Firstly,it transformed the user-project scoring matrix into user-project bipartite graphs according to the bipartite graph network of complex network theory. It used the conditional walk to compute the similarity between the target user and other users,and then scored unrated items according to the collaborative filtering algorithm and generated recommendations. The results show that under the same data sparsity condition,the co-filtering algorithm based on conditional bipartite map is superior to the other two traditional algorithms in MAE and accuracy,and the algorithm's recommendation accuracy is improved. The higher the degree of data sparsity,the higher the recommendation quality of the recommendation algorithm based on conditional biped graph.