基于矩阵分解的个性化推荐系统研究

随着社交网络的快速发展,用户在使用社交应用时会产生大量有价值的数据。通过对社交网络进行数据挖掘,发现隐藏在数据中关联用户与物品之间的偏好关系。然后对用户建模分析,选择合适的推荐引擎进行个性化物品推荐,这是一个非常有价值的研究方向。该文重点研究矩阵分解算法对处理大规模用户与物品评分矩阵的推荐效果,为了提高推荐的准确度展开了对用户社交关系和隐性反馈的研究,在组合预测模型中加入社交关系、人口统计学信息配置项、用户的消费记录等隐因子项,通过实验验证了扩展之后的混合预测模型在RMSE值上比SVD算法降低了0.259 475,在推荐性能有较大幅度的提高。

Personalized Recommender System Based on Matrix Factorization

With the development of social network, there is a large amount of valuable information produced by social network users. This paper focused on the personification recommender system based on matrix factorization. In order to improve the recommender systems, we study the user social relationship and the implicit feedback of user. We add in the matrix factorization optimization function by a social regularization, a demographic information configuration item, and the consumer records as items latent factor bias. Experiments indicates a decrease in RMSE by 0.259475 achieved by the proposed method than SVD algorithm.