[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),2022,22(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),2022,22(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:
22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1; 2; 曹 原1; 2
(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 Wenqian1; 2; Cao Yuan1; 2
(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 recommendation; convolutional neural network; deep learning; preference fusion; neural 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.

参考文献/References:

[1] ZHANG J,GAO C,JIN D P,et al. Group-buying recommendation for social e-commerce[C]//Proceedings of the 2021 IEEE 37th International Conference on Data Engineering(ICDE). Chania,Greece:IEEE,2021.
[2]RENDLE S. Factorization machines[C]//2010 IEEE International Conference on Data Mining. Sydney,Australia:IEEE,2010.
[3]CHENG H T,KOC L,HARMSEN J,et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Boston,USA:ACM,2016.
[4]GUO H,TANG R,YE Y,et al. DeepFM:a factorization-machine based neural network for CTR prediction[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne,Australia:AAAI Press,2017.
[5]ZHOU G R,SONG C R,ZHU X Q,et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London,UK:ACM,2018.
[6]CAO D,HE X N,MIAO L H,et al. Attentive group recommendation[C]//The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. Ann Arbor,USA:ACM,2018.
[7]HE X N,LIAO L Z,ZHANG H W,et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web. Perth,Australia:IWWWCSC,2017.
[8]吴云昌,刘柏嵩,王洋洋,等. 群组推荐分析与研究综述[J]. 电信科学,2018,34(12):71-83.
[9]MIKOLOV T,CHEN K,CORRADO G,et al. Efficient estimation of word representations in vector space[C]//Proceedings of the 2013 International Conference on Learning Representation(ICLR2013). Scottsdale,USA:ICLR,2013.
[10]ZHANG Y,WALLACE B. A sensitivity analysis of(and practitioners’ guide to)convolutional neural networks for sentence classification[J]. arXiv preprint arXiv:1510.03820,2015.
[11]LECUN Y,BOTTOU L,BENGIO Y,et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE,1998,86(11):2278-2324.
[12]LIU B,TANG R M,CHEN Y Z,et al. Feature generation by convolutional neural network for click-through rate prediction[C]//Proceedings of the World Wide Web Conference 2019. San Francisco,USA:ACM,2019.
[13]COVINGTON P,ADAMS J,SARGIN E. Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems. Boston,USA:ACM,2016.
[14]CAO D,HE X N,MIAO L H,et al. Social-enhanced attentive group recommendation[J]. IEEE Transactions on Knowledge and Data Engineering,2021,33(3):1195-1209.
[15]葛尧,陈松灿. 面向推荐系统的图卷积网络[J]. 软件学报,2020,31(4):1101-1112.
[16]TANG J X,WANG K. Personalized top-n sequential recommendation via convolutional sequence embedding[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. Marina Del Rey,USA:ACM,2018.
[17]燕存,吉根林. Item-Based并行协同过滤推荐算法的设计与实现[J]. 南京师大学报(自然科学版),2014,37(1):71-75.
[18]LIU Q,YU F,WU S,et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne,Australia:ACM,2015.
[19]王俊淑,张国明,胡斌. 基于深度学习的推荐算法研究综述[J]. 南京师范大学学报(工程技术版),2018,18(4):33-43.
[20]GUO C,LI B,TIAN X M. Flickr group recommendation using rich social media information[J]. Neurocomputing,2016,204:8-16.

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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