融合邻域模型与矩阵分解模型的推荐算法

协同过滤推荐算法是目前构建推荐系统最为成功的算法之一,它利用已知的一组用户对物品喜好数据来对推测用户对其他物品的喜好,其中,能够直接刻画用户与项目潜在特征的矩阵分解模型和通过分析物品或者项目间相似度的邻域模型是研究的热点.针对这两个模型存在的不足,提出了一种将邻域模型与矩阵分解模型有效结合的方法,进而构建了一个改进的协同过滤推荐算法,提高了预测准确性.实验结果验证了改进算法的正确性与有效性.

Recommender Algorithm Incorporating Neighborhood Model with Matrix Factorization

Collaborative Filtering(CF) is one of the most successful approaches for building recommender system,it uses the known preferences of a group of users to make predictions of unknown preferences of other users. The matrix factorization models which can profile both users and items latent factors directly,and the neighborhood models which can analyze similarities between users and items are current research focuses.A method of merging both matrix factorization models and neighborhood models is proposed, which can make further accuracy improvements. The experiment results show that this method is correct and feasible.