融合主题信息和卷积神经网络的混合推荐算法

doi:10.11772/j.issn.1001-9081.2019122067

计算机应用 ›› 2020, Vol. 40 ›› Issue (7): 1901-1907.DOI: 10.11772/j.issn.1001-9081.2019122067

融合主题信息和卷积神经网络的混合推荐算法

田保军¹, 刘爽², 房建东¹

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

收稿日期:2019-12-09 修回日期:2020-02-17 出版日期:2020-07-10 发布日期:2020-05-14
通讯作者: 田保军
作者简介:田保军(1971-),男,内蒙古呼和浩特人,副教授,硕士,主要研究方向:机器学习、推荐系统;刘爽(1993-),女,山东菏泽人,硕士研究生,主要研究方向:推荐系统;房建东(1966-),女,内蒙古呼和浩特人,教授,博士,主要研究方向:信息处理、智能控制。
基金资助:
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）。

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

TIAN Baojun¹, LIU Shuang², FANG Jiandong¹

1. College of Information Engineering, Inner Mongolia University of Technology, Hohhot Inner Mongolia 010080, China;
2. College of Data Science and Application, Inner Mongolia University of Technology, Hohhot Inner Mongolia 010080, China

Received:2019-12-09 Revised:2020-02-17 Online:2020-07-10 Published:2020-05-14
Supported by:
This work is partially supported by the Natural Science Foundation of Inner Mongolia Autonomous Region (2019MS06024, 2019MS06023), the Science and Technology Major Project of Inner Mongolia Autonomous Region (2018ZD0302), the Science and Technology Program of Inner Mongolia Autonomous Region (20170306).

摘要/Abstract

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。

Abstract: 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 1M, Movielens 10M and Amazon. The experimental results show that, compared to PMF, CDL and ConvMF models, on the Movielens 1M 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% and 4.03%, 1.46% and 2.00% respectively; on the Movielens 10M dataset, the RMSE and MAE of LCPMF are reduced by 5.35% 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.

Key words: recommendation algorithm, topic model, Convolutional Neural Network (CNN), Probability Matrix Factorization (PMF), collaborative filtering

中图分类号:

TP183

田保军, 刘爽, 房建东. 融合主题信息和卷积神经网络的混合推荐算法[J]. 计算机应用, 2020, 40(7): 1901-1907.

TIAN Baojun, LIU Shuang, FANG Jiandong. Hybrid recommendation algorithm by fusion of topic information and convolution neural network[J]. Journal of Computer Applications, 2020, 40(7): 1901-1907.

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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

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