融合主题信息和卷积神经网络的混合推荐算法  

Hybrid recommendation algorithm by fusion of topic information and convolution neural network

作　　者：田保军[1] 刘爽 房建东[1] TIAN Baojun;LIU Shuang;FANG Jiandong(College of Information Engineering,Inner Mongolia University of Technology,Hohhot Inner Mongolia 010080,China;College of Data Science and Application,Inner Mongolia University of Technology,Hohhot Inner Mongolia 010080,China)

机构地区：[1]内蒙古工业大学信息工程学院,呼和浩特010080 [2]内蒙古工业大学数据科学与应用学院,呼和浩特010080

出　　处：《计算机应用》2020年第7期1901-1907,共7页journal of Computer Applications

基　　金：内蒙古自治区自然科学基金资助项目(2019MS06024,2019MS06023);内蒙古自治区科技重大项目(2018ZD0302);内蒙古自治区科技计划项目(20170306)。

摘　　要：针对传统的协同过滤算法中数据稀疏和推荐结果不准确的问题,提出了一种基于隐狄利克雷分布(LDA)与卷积神经网络(CNN)的概率矩阵分解推荐模型(LCPMF),该模型综合考虑项目评论文档的主题信息与深层语义信息。首先,分别使用LDA主题模型和文本CNN对项目评论文档建模;然后,获取项目评论文档的显著潜在低维主题信息及全局深层语义信息,从而捕获项目文档的多层次特征表示;最后,将得到的用户和多层次的项目特征融合到概率矩阵分解(PMF)模型中,产生预测评分进行推荐。在真实数据集Movielens 1M、Movielens 10M与Amazon上,将LCPMF与经典的PMF、协同深度学习(CDL)、卷积矩阵因子分解模型(ConvMF)模型进行对比。实验结果表明,相较PMF、CDL、ConvMF模型,所提推荐模型LCPMF的均方根误差(RMSE)和平均绝对误差(MAE)在Movielens 1M数据集上分别降低了6.03%和5.38%、5.12%和4.03%、1.46%和2.00%,在Movielens 10M数据集上分别降低了5.35%和5.67%、2.50%和3.64%、1.75%和1.74%,在Amazon数据集上分别降低17.71%和23.63%、14.92%和17.47%、3.51%和4.87%,验证了所提模型在推荐系统中的可行性与有效性。Aiming at the problems of data sparsity and inaccuracy of recommendation results in the traditional collaborative filtering algorithms,a Probability Matrix Factorization recommendation model based on Latent Dirichlet Allocations(LDA)and Convolutional Neural Network(CNN)named LCPMF was proposed,which considers the topic information and deep semantic information of project review document comprehensively.Firstly,the LDA topic model and the text CNN were used to model the project review document respectively.Then,the significant potential low-dimensional topic information and the global deep semantic information of project review document were obtained in order to capture the multi-level feature representation of the project document.Finally,the obtained features of users and multi-level projects were integrated into the Probability Matrix Factorization(PMF)model to generate the prediction score for recommendation.LCPMF was compared with the classical PMF,Collaborative Deep Learning(CDL)and Convolutional Matrix Factorization(ConvMF)models on the real datasets Movielens 1 M,Movielens 10 M and Amazon.The experimental results show that,compared to PMF,CDL and ConvMF models,on the Movielens 1 M dataset,the Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)of the proposed recommender model LCPMF are reduced by 6.03%and 5.38%,5.12%and4.03%,1.46%and 2.00%respectively;on the Movielens 10 M dataset,the RMSE and MAE of LCPMF are reduced by5.35%and 5.67%,2.50%and 3.64%,1.75%and 1.74%respectively;while on the Amazon dataset,the RMSE and MAE of LCPMF are reduced by 17.71%and 23.63%,14.92%and 17.47%,3.51%and 4.87%respectively.The feasibility and effectiveness of the proposed model in the recommendation system are verified.

关 键 词：推荐算法 主题模型 卷积神经网络 概率矩阵分解 协同过滤 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]