[1]尚文倩,曹 原.FastGR:一种基于神经协同过滤的群组推荐算法[J].南京师范大学学报(工程技术版),2022,(02):029-34.[doi:10.3969/j.issn.1672-1292.2022.02.005]
 Shang Wenqian,Cao Yuan.FastGR:A Group Recommendation Algorithm Based on Neural Collaborative Filtering[J].Journal of Nanjing Normal University(Engineering and Technology),2022,(02):029-34.[doi:10.3969/j.issn.1672-1292.2022.02.005]
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FastGR:一种基于神经协同过滤的群组推荐算法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2022年02期
页码:
029-34
栏目:
计算机科学与技术
出版日期:
2022-06-30

文章信息/Info

Title:
FastGR:A Group Recommendation Algorithm Based on Neural Collaborative Filtering
文章编号:
1672-1292(2022)02-0029-06
作者:
尚文倩12曹 原12
(1.中国传媒大学媒体融合与传播国家重点实验室,北京 100024)(2.中国传媒大学计算机与网络空间安全学院,北京 100024)
Author(s):
Shang Wenqian12Cao Yuan12
(1.State Key Laboratory of Media Convergence and Communication,Communication University of China,Beijing 100024,China)(2.School of Computer Science and Cyber Sciences,Communication University of China,Beijing 100024,China)
关键词:
群组推荐算法卷积神经网络深度学习偏好融合神经协同过滤
Keywords:
group recommendationconvolutional neural networkdeep learningpreference fusionneural collaborative filtering
分类号:
TP181
DOI:
10.3969/j.issn.1672-1292.2022.02.005
文献标志码:
A
摘要:
群组推荐问题的关键在于如何对组内各成员不同的偏好进行融合来适应所有成员的需求. 基于神经协同过滤框架和注意力机制的群组推荐算法从数据中动态地学习融合策略,相较于传统基于预定义策略的方法明显提升了推荐效果,但模型训练及推理时间较长. 本文在此基础上重构了群组偏好融合模块,引入卷积神经网络来提取群组成员的特征,从而实现偏好融合:在公开数据集上的实验表明,本文算法比现有的算法具有更优的精度,训练速度提高了14倍.
Abstract:
The key problem of group recommendation is how to integrate the different preferences of group members to meet the needs of all members. The group recommendation algorithm based on neural collaborative filtering framework and attention mechanism dynamically learns fusion strategy from the data,significantly improving the recommendation effect compared with the traditional predefined strategy based method,but the model training and infer time is longer. In order to achieve the convergence of preferences,we reconstruct the group preference fusion module by adopting the convolution neural network to extract the feature of the group members. Experiments on open data sets show that the algorithm in this paper has better accuracy and improved the training speed by 14 times than that of the current algorithm.

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备注/Memo

备注/Memo:
收稿日期:2021-08-31.
基金项目:国家重点研发计划项目(2018YFB0803701-1)、中国传媒大学中央高校基本科研业务费专项资金项目.
通讯作者:尚文倩,博士,教授,研究方向:机器学习. E-mail:shangwenqian@cuc.edu.cn
更新日期/Last Update: 1900-01-01