Recommendation model for user attribute preference modeling based on convolutional neural network interaction

doi:10.11772/j.issn.1001-9081.2021041070

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (2): 404-411.DOI: 10.11772/j.issn.1001-9081.2021041070

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

Recommendation model for user attribute preference modeling based on convolutional neural network interaction

Renzhi PAN¹^,²^,³, Fulan QIAN¹^,²^,³(), Shu ZHAO¹^,²^,³, Yanping ZHANG¹^,²^,³

^1.School of Computer Science and Technology，Anhui University，Hefei Anhui 230601，China
^2.Key Laboratory of Intelligent Computing and Signal Processing，Ministry of Education （Anhui University），Hefei Anhui 230601，China
^3.Information Materials and Intelligent Sensing Laboratory of Anhui Province （Anhui University），Hefei Anhui 230601，China

Received:2021-06-22 Revised:2021-07-09 Accepted:2021-07-09 Online:2022-02-11 Published:2022-02-10
Contact: Fulan QIAN
About author:PAN Renzhi， born in 1996， M. S. candidate. His research interests include deep learning， recommender system.
QIAN Fulan， born in 1978， Ph. D.， associate professor. Her research interests include granular computing， social network， recommender system.
ZHAO Shu， born in 1979， Ph. D.， professor. Her research interests include granular computing， quotient space theory， machine learning.
ZHANG Yanping， born in 1962， Ph. D.， professor. Her research interests include intelligent computing， granular computing， quotient space theory.
Supported by:
Natural Science Foundation of Anhui Province(1808085MF175)

基于卷积神经网络交互的用户属性偏好建模的推荐模型

潘仁志¹^,²^,³, 钱付兰¹^,²^,³(), 赵姝¹^,²^,³, 张燕平¹^,²^,³

^1.安徽大学计算机科学与技术学院, 合肥 230601
^2.计算智能与信号处理教育部重点实验室(安徽大学), 合肥 230601
^3.信息材料和智能感知安徽省实验室(安徽大学), 合肥 230601

通讯作者: 钱付兰
作者简介:潘仁志（1996—），男，安徽马鞍山人，硕士研究生，主要研究方向：深度学习、推荐系统；
钱付兰（1978—），女，安徽蚌埠人，副教授，博士，主要研究方向：粒计算、社交网络、推荐系统；
赵姝（1979—），女，安徽巢湖人，教授，博士，主要研究方向：粒计算、商空间理论、机器学习；
张燕平（1962—），女，安徽合肥人，教授，博士，主要研究方向：智能计算、粒计算、商空间理论。
基金资助:
安徽省自然科学基金资助项目(1808085MF175)

Abstract

Abstract:

Latent Factor Model （LFM） have been widely used in recommendation field due to their excellent performance. In addition to interactive data， auxiliary information is also introduced to solve the problem of data sparsity， thereby improving the performance of recommendations. However， most LFMs still have some problems. First， when modeling users by LFM， how users make decisions on items based on their feature preferences is ignored. Second， the feature interaction using inner product assumes that the feature dimensions are independent to each other， without considering the correlation between the feature dimensions. In order to solve the above problems， a recommendation model for User Attribute preference Modeling based on Convolutional Neural Network （CNN） interaction （UAMC） was proposed. In this model， the general preferences of users， user attributes and item embeddings were firstly obtained， and then the user attributes and item embeddings were interacted to explore the preferences of different attributes of users to different items. After that， the interacted user preference attributes were sent to the CNN layer to explore the correlation between different dimensions of different preference attributes and thus obtain the users’ attribute preference vectors. Next， the attention mechanism was used to combine the general preferences of the users with the attribute preferences obtained from CNN layer to obtain the vector representations of the users. Finally， the dot product was used to calculate the users’ ratings of the items. Experiments were conducted on three real datasets： Movielens-100K， Movielens-1M and Book-crossing. The results show that the proposed algorithm decreases the Root Mean Square Error （RMSE） by 1.75%， 2.78% and 0.25% respectively compared with the model of Neural Factorization Machine for sparse predictive analytics （NFM）， which verifies the effectiveness of UAMC model in improving the accuracy of recommendation in the rating prediction recommendation of LFM.

Key words: Latent Factor Model (LFM), user preference, user attribute preference, Convolutional Neural Network (CNN), feature interaction, attention mechanism

摘要：

潜在因子模型（LFM）以其优异的性能在推荐领域得到了广泛应用。在LFM中除了使用交互数据以外，辅助信息也被引入用于解决数据稀疏的问题，从而提升推荐的性能。然而，大多数LFM仍然存在一些问题：第一，LFM在对用户进行建模时，忽略了用户如何根据其特征偏好对项目作出决策；第二，采用内积的特征交互假设特征维度之间是相互独立的，而没有考虑到特征维度之间的关联。针对上述问题，提出一种新的推荐模型：基于卷积神经网络（CNN）交互的用户属性偏好建模的推荐模型（UAMC）。该模型首先获得用户的一般偏好、用户属性和项目嵌入，然后将用户属性和项目嵌入进行交互，以探索用户不同的属性对不同项目的偏好；接着将交互过的用户偏好属性送入CNN层来探索不同偏好属性的不同维度的关联，从而得到用户的属性偏好向量；接着使用注意力机制结合用户的一般偏好和CNN层得到的属性偏好，从而获得用户的向量表示；最后采用点积来计算用户对项目的评分。在Movielens-100K、Movielens-1M和Book-crossing这三个真实的数据集上进行了实验。实验结果表明，所提模型在均方根误差（RMSE）上与稀疏数据预测的神经网络分解机（NFM）模型相比分别降低了1.75%、2.78%和0.25%，验证了在LFM的评分预测推荐中，UAMC在提升推荐精度上的有效性。

关键词: 潜在因子模型, 用户偏好, 用户属性偏好, 卷积神经网络, 特征交互, 注意力机制

CLC Number:

TP183

Renzhi PAN, Fulan QIAN, Shu ZHAO, Yanping ZHANG. Recommendation model for user attribute preference modeling based on convolutional neural network interaction[J]. Journal of Computer Applications, 2022, 42(2): 404-411.

潘仁志, 钱付兰, 赵姝, 张燕平. 基于卷积神经网络交互的用户属性偏好建模的推荐模型[J]. 《计算机应用》唯一官方网站, 2022, 42(2): 404-411.

Figures/Tables 6

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数据集	用户数	项目数	评分数	稀疏度
Movielens-100K	943	1 682	100 000	0.062 94
Movielens-1M	6 040	3 952	1000 000	0.041 89
Book-crossing	5 004	110 311	200 000	0.000 36

数据集	用户数	项目数	评分数	稀疏度
Movielens-100K	943	1 682	100 000	0.062 94
Movielens-1M	6 040	3 952	1000 000	0.041 89
Book-crossing	5 004	110 311	200 000	0.000 36

模型	Movielens-100K		Movielens-1M		Book-crossing
模型	RMSE	MAE	RMSE	MAE	RMSE	MAE
MF	0.912 7	0.713 3	0.869 3	0.681 5	1.554 6	1.213 8
NCF	0.930 6	0.735 7	0.882 3	0.693 1	1.564 1	1.237 2
NFM	0.913 4	0.718 9	0.878 2	0.687 5	1.552 4	1.208 5
JICO	0.942 8	0.740 1	0.862 6	0.6591	—	—
HGAR	0.921 6	0.717 7	0.856 9	0.668 7	1.549 3	1.202 5
MCCF	0.907 0	0.705 0	—	—	—	—
UAMC	0.8974	0.7047	0.8538	0.670 8	1.5485	1.2005

模型	Movielens-100K		Movielens-1M		Book-crossing
模型	RMSE	MAE	RMSE	MAE	RMSE	MAE
MF	0.912 7	0.713 3	0.869 3	0.681 5	1.554 6	1.213 8
NCF	0.930 6	0.735 7	0.882 3	0.693 1	1.564 1	1.237 2
NFM	0.913 4	0.718 9	0.878 2	0.687 5	1.552 4	1.208 5
JICO	0.942 8	0.740 1	0.862 6	0.6591	—	—
HGAR	0.921 6	0.717 7	0.856 9	0.668 7	1.549 3	1.202 5
MCCF	0.907 0	0.705 0	—	—	—	—
UAMC	0.8974	0.7047	0.8538	0.670 8	1.5485	1.2005

数据集	UAMC		UAMC-C
数据集	RMSE	MAE	RMSE	MAE
Movielens-100K	0.897 4	0.704 7	0.918 4	0.724 2
Movielens-1M	0.853 8	0.670 8	0.859 9	0.676 7
Book-crossing	1.548 5	1.200 5	1.550 2	1.202 7

Recommendation model for user attribute preference modeling based on convolutional neural network interaction

基于卷积神经网络交互的用户属性偏好建模的推荐模型

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