[1]宋志理,胡胜利,王峰.基于深度学习特征表示协同过滤算法[J].常州大学学报(自然科学版),2021,33(01):62-69.[doi:10.3969/j.issn.2095-0411.2021.01.010]
　SONG Zhili,HU Shengli,WANG Feng.Research on Cooperative Filtering Algorithm Based on Deep Learning Feature Representation[J].Journal of Changzhou University(Natural Science Edition),2021,33(01):62-69.[doi:10.3969/j.issn.2095-0411.2021.01.010]
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基于深度学习特征表示协同过滤算法()
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常州大学学报(自然科学版)[ISSN:2095-0411/CN:32-1822/N]

卷:
第33卷
期数:
2021年01期
页码:
62-69
栏目:
计算机与信息工程
出版日期:
2021-01-20

文章信息/Info

Title:
Research on Cooperative Filtering Algorithm Based on Deep Learning Feature Representation
文章编号:
2095-0411(2021)01-0062-08
作者:
宋志理1; 胡胜利1; 王峰2
(1.安徽理工大学 计算机科学与工程学院, 安徽 淮南 232001; 2.阜阳师范学院 计算机与信息工程学院, 安徽 阜阳 236037)
Author(s):
SONG Zhili1;  HU Shengli1;  WANG Feng2
(1. School of Computer Science and Engineering,Anhui University of Science and Technology, Huainan 232001, China; 2. School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China)
关键词:
协同过滤;  多层感知机;  卷积神经网络;  深度学习
Keywords:
collaborative filtering;  multi-layer perceptron;  convolutional neural network;  deep learning
分类号:
TP 393
DOI:
10.3969/j.issn.2095-0411.2021.01.010
文献标志码:
A
摘要:
在推荐系统中,单一的学习矩阵分解的内积交互或者利用深度神经网络来捕获用户与项目交互,不足以有效地学习用户与项目的潜在特征。针对这一问题,提出一种在显式反馈与隐式反馈基础上,称为基于深度学习特征表示的协同过滤算法(DLFeaCF)。该模型首先学习用户与项目的内积与外积交互; 然后在内积的基础上,从隐式映射与特征映射两个方面再利用多层感知机(MLP)的非线性交互学习能力去获取用户与项目的全局特征; 同时在外积的基础上,利用CNN学习捕获用户与项目的局部特征; 最后在融合层组合特征并获得预测分数。在真实的MovieLens数据集上进行实验,表明DLFeaCF模型能获得更好的推荐性能。
Abstract:
In the recommendation system, the inner product interaction of a single learning matrix decomposition or the use of deep neural networks to capture user interaction with the project is not sufficient to effectively learn the potential characteristics of users and projects. In view of this problem, this paper proposes a collaborative filtering algorithm based on deep learning feature representation(DLFeaCF)based on display feedback and implicit feedback. The model first learns the inner product and outer product interaction between the user and the project. Then, based on the inner product, it uses the nonlinear interactive learning ability of the multi-layer perceptron(MLP)to obtain the two aspects from implicit mapping and feature mapping; on the basis of the outer product, CNN learning is used to capture the local features of the user and the project; finally, combine the features in the fusion layer and obtain the prediction score.The experiments on the real MovieLens dataset show that the DLFeaCF model can achieve better recommendation performance.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2020-08-16。
基金项目:阜阳市政府横向合作科研资助项目(XDHX2016018)。
作者简介:宋志理(1993—),女,安徽宣城人,硕士生。通信联系人:胡胜利(1978—),E-mail:slhu@aust.edu.cn
更新日期/Last Update: 2021-01-20