SCMF:一种融合多源数据的软约束矩阵分解推荐算法

SCMF:A Matrix Factorization Model With Soft Constraint for Multi-Source Recommendation

数据稀疏是推荐系统面临的主要挑战之一。近年来,多源数据融合为解决数据稀疏问题提供了新思路。然而,现有方法大多假设对象在不同数据源中具有相同的表示,这种硬约束方式无法刻画对象在不同数据源中的差异性。该文提出一种基于软约束矩阵分解的推荐算法,通过约束不同数据源中对象的隐因子向量,能够同时刻画同一对象表示的共性及其在不同数据源中的差异性。在两个数据集上的实验表明,该文提出的软约束矩阵分解算法在准确率方面优于现有的单数据源推荐算法和多源数据硬约束融合推荐算法,可以有效解决推荐系统面临的数据稀疏问题。

Data sparsity is a challenge forrecommender systems.In recent years,the integration of data from different sources provides a promising direction for the solution of this issue.However,most existing methods for data integration assume that the representation of a single user/item is the same across different contexts,which blocksthe depiction of the distinct characteristics of different contexts.In this paper,we propose a matrix factorization model with soft constraint that the difference between the representations of a single user/item is minimized together with the error function of matrix factorization model.Experiments on two datasets demonstrate that the proposed model outperforms thestate-of-the-art models,especially on the case where the data is sparse in only one resource.