基于神经网络和社区发现的高维数据推荐系统

RECOMMENDATION SYSTEM OF HIGH DIMENSIONAL DATA BASED ON NEURAL NETWORKS AND COMMUNITY DISCOVERY

由于评分矩阵存在稀疏性问题和冷启动问题,传统的推荐系统大多通过分析上下文环境来增强推荐系统的性能,导致计算复杂度提高,并影响推荐的准确率。针对这种情况,提出基于神经网络和社区发现的高维数据推荐系统。利用神经网络识别影响力大的上下文维度,提高预测的准确率;设计社区检测算法将用户分组,降低数据维度并解决稀疏性问题;采用张量模型处理包含丰富附加信息的用户评分矩阵,根据张量值预测用户对项目的偏好。仿真实验结果表明,该系统有效地提高了高维数据推荐系统的性能。

Because of the existence of sparsity problem and cold start problem of rating matrix,most of classical recommendation systems enhance the performance by analyzing context environment,which leads to the increase of computational complexity and affects the accuracy of recommendation.In view of this,we propose a recommendation system of high dimensional data based on neural networks and community discovery.It used neural networks to recognize influential context dimensions,and improved the prediction accuracy;a community detection algorithm was designed to group users,and it could reduce data dimension and solve the sparsity problem;the tensor model was used to process the user rating matrix with additional information,and the user preference for the project was predicted according to the tensor value.The simulation results show that the system effectively improves the performance of high-dimensional data recommendation system.