受限玻尔兹曼机与加权Slope One的混合推荐算法研究

Research on hybrid recommendation algorithm of restricted Boltzmann machine and weighted Slope One

针对传统协同过滤算法所面临的稀疏性及预测准确度不高的问题,提出一种基于受限玻尔兹曼机与加权Slope One的混合推荐算法。首先通过受限玻尔兹曼机对评分矩阵的初步填充,缓解数据的稀疏性问题;然后通过一种混合项目相似度计算方法,引入项目属性信息;最后通过加权Slope One算法的二次预测,提升推荐效果。在MovieLens100K数据集上的实验表明,两种算法的结合提高了推荐的准确度。

Aiming at the sparseness and low prediction accuracy of traditional collaborative filtering algorithms,this paper proposed a hybrid recommendation algorithm based on restricted Boltzmann machine and weighted Slope One.Firstly,it used the preliminary filling of the scoring matrix by the restricted Boltzmann machine to alleviate the sparseness problem of the data.Then,it introduced the project attribute information through a hybrid project similarity calculation method.Finally,it adopted the second prediction by the weighted Slope One algorithm to improve the recommended effect.Experiments on the Mo-vieLens100K dataset show that the combination of the two algorithms increases the accuracy of the recommendation.