基于协同过滤和深度学习的信息分析算法

Information analysis algorithm based on collaborative filtering and deep learning

针对在结构复杂的大型人力资源数据库中,传统协同过滤算法存在冷启动的问题,文中开发了一种数据信息的综合分析算法。该算法在传统协同过滤算法的基础上融合了深度学习模型,采用栈式去噪自编码器和概率矩阵分解模型求出项目的隐语义矩阵。同时还构建了相关项目的评分矩阵,并利用该矩阵对项目得分加以预测,且将其作为冷启动项目的分数。通过协同过滤算法进行项目推荐,从而解决了算法的冷启动问题,并提高了综合性能。实验测试结果表明,文中所提算法较传统协同过滤算法的推荐准确率提高了约20%。

In view of the cold start problem of the traditional collaborative filtering algorithm in the large⁃scale human resources database with complex structure,a comprehensive analysis algorithm of data information is developed in this paper.Based on the traditional collaborative filtering algorithm,the algorithm integrates the deep learning model,solves the project’s cryptic matrix through the trestle de⁃noising self encoder and the probability matrix decomposition model,and constructs the scoring matrix of related projects.The scoring matrix is used to predict the score of the project,which is taken as the score of the cold start project,and then the collaborative filtering algorithm is used to recommend the project,thus solving the cold start problem of the collaborative algorithm and improving the comprehensive performance of the algorithm.Experimental results show that the proposed algorithm improves the recommendation accuracy by about 20%compared with the traditional collaborative filtering algorithm.