基于Logistic函数的社会化矩阵分解推荐算法

持续指数增长的互联网逐渐带来了信息过载问题,使得推荐系统提供的信息过滤服务尤为重要. 协同过滤是推荐系统领域最为成功的技术,但依然存在数据稀疏性等问题. 社会关系信息能够有效提高推荐系统的预测准确性. 为解决数据稀疏性问题,本文提出了一种利用Logistic函数的社会化矩阵分解推荐算法. 在3组真实数据结合上的实验结果表明,本文提出的算法能够提供更准确的推荐结果,特别是在数据稀疏的情况下,显著缓解了数据稀疏性问题. 

A Social Matrix Factorization Recommender Algorithm Based on Logistic Function

The ongoing exponential growth of the Internet brings an information overload, which greatly increases the necessity of effective recommender systems for information filtering. However, collaborative filtering, which is recognized as the most successful technique in designing recommender systems, still encounters the data sparsity problem. Social relations have been found to be effective to improve the prediction accuracy of recommender systems. In order to handle the data sparsity problem, this paper proposed a new social matrix factorization recommender algorithm by leveraging the Logistic function. Experimental results on three real-world datasets illustrate that the proposed method provides more accurate recommendation results, especially under sparse conditions.