[1]鲍维克,袁春.面向推荐系统的分期序列自注意力网络[J].智能系统学报,2021,16(2):353-361.[doi:10.11992/tis.202005028]
 BAO Weike,YUAN Chun.Recommendation system with long-term and short-term sequential self-attention network[J].CAAI Transactions on Intelligent Systems,2021,16(2):353-361.[doi:10.11992/tis.202005028]
点击复制

面向推荐系统的分期序列自注意力网络(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年2期
页码:
353-361
栏目:
学术论文—人工智能基础
出版日期:
2021-03-05

文章信息/Info

Title:
Recommendation system with long-term and short-term sequential self-attention network
作者:
鲍维克1 袁春2
1. 清华大学 计算机科学与技术系,北京 100084;
2. 清华大学 深圳国际研究生院,广东 深圳 518000
Author(s):
BAO Weike1 YUAN Chun2
1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2. Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China
关键词:
推荐系统序列推荐注意力机制动态赋权自注意力机制序列依赖关系门控循环单元序列性偏好
Keywords:
recommendation systemsequence recommendationattention modeldynamic weightingself-attention modelsequence dependenceGRUsequential preference
分类号:
TP391
DOI:
10.11992/tis.202005028
摘要:
在推荐系统中,为了充分表达用户反馈数据内部的相互依赖和序列性,准确提取用户的长期/一般偏好、应对数据的动态性,本文提出了一种分期序列自注意力网络(long-term & short-term sequential self-attention network,LSSSAN)进行序列推荐。模型采用自注意力机制和GRU捕捉了用户反馈数据之间的相互依赖和序列性;模型采用注意力机制为不同反馈数据赋予不同权重以动态捕捉重点信息,同时考虑了上下文的动态性;模型基于用户的长期反馈数据,准确表达了用户的长期/一般偏好。该模型在两个数据集上进行训练和测试,结果表明该模型的推荐效果整体优于之前的相关工作。
Abstract:
To fully express the internal interdependence, user interaction data sequentiality, and long-term or general preferences and deal with the dynamics of data, this paper proposes the long-term and short-term sequential self-attention network (LSSAN) for sequential recommendation in the recommendation system, and the LSSSAN model. This model uses self-attention and a GRU to capture the dependence and sequentiality among the user’s data. Moreover, the model uses Attention Net to combine user characteristics and the candidate item set for recommendation as context for capturing the dynamics of the recommendation task. The model accurately expresses the general preferences of users based on their long-term interaction data. We train and test the LSSSAN on two data sets, and its effect is generally better than that of the previous work.

参考文献/References:

[1] 孙宏超. 阿里巴巴发布2020财年第三季度财报:收入增长38%, 年活跃用户达7亿[EB/OL].[2020-02-13]. kuaibao.qq.com/s/20200213A0PEAW00
[2] WANG Shoujin, HU Liang, WANG Yan, et al. Sequential recommender systems:challenges, progress and prospects[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China, 2019:6332-6338.
[3] XU Chen, XU Hongteng, ZHANG Yongfeng, et al. Sequential recommendation with user memory networks[C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Marina Del Rey, USA, 2018:108-116.
[4] KOREN Y. Collaborative filtering with temporal dynamics[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France, 2009:447-456.
[5] GARCIN F, DIMITRAKAKIS C, FALTINGS B. Personalized news recommendation with context trees[C]//Proceedings of the 7th ACM Conference on Recommender Systems. Hong Kong, China, 2013:105-112.
[6] RENDLE S, FREUDENTHALER C, SCHMIDT-THIEME L. Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the 19th International Conference on World Wide Web. Raleigh, USA, 2010:811-820.
[7] HIDASI B, TIKK D. General factorization framework for context-aware recommendations[J]. Data mining and knowledge discovery, 2016, 30(2):342-371.
[8] HE RUINING, MCAULEY J. Fusing similarity models with Markov chains for sparse sequential recommendation[C]//Proceedings of the 2016 IEEE 16th International Conference on Data Mining. Barcelona, Spain, 2016:191-200.
[9] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[C]//Proceedings of the 4th International Conference on Learning Representations. San Juan, Puerto Rico, 2016:1-10.
[10] WU Chaoyuan, AHMED A, BEUTEL A, et al. Recurrent recommender networks[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Cambridge, UK, 2017:495-503.
[11] TANG Jiaxi, BELLETTI F, JAIN S, et al. Towards neural mixture recommender for long range dependent user sequences[C]//Proceedings of World Wide Web Conference. San Francisco, USA, 2019:1782-1793.
[12] ZHOU Guorui, ZHU Xiaoqiang, SONG Chenru, 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, 2018:1059-1068.
[13] ZHANG Shuai, TAY Y, YAO Lina, et al. 2019. Next item recommendation with self-attentive metric learning[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Hawaii, USA, 2019:9.
[14] YING Haochao, ZHUANG Fuzhen, ZHANG Fuzheng, et al. Sequential recommender system based on hierarchical attention networks[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden, 2018:3926-3932.
[15] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, USA, 2017:5998-6008.
[16] CHO K, VAN MERRI?NBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar, 2014:1724-1734.
[17] PAN Rong, ZHOU Yunhong, CAO Bin, et al. One-class collaborative filtering[C]//Proceedings of the 20088th IEEE International Conference on Data Mining. Pisa, Italy, 2008:502-511.
[18] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR:Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. Montreal, Canada, 2009:452-461.
[19] HU Liang, CAO Longbing, WANG Shoujin, et al. Diversifying personalized recommendation with user-session context[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne, Australia, 2017:1858-1864.
[20] CHO E, A MYERS S A, LESKOVEC J. Friendship and mobility:user movement in location-based social networks[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, USA, 2011:1082-1090.
[21] WANG Pengfei, GUO Jiafeng, LAN Yanyan, et al. Learning hierarchical representation model for NextBasket recommendation[C]//Proceedings of the 38th International ACM SIGIR conference on Research and Development in Information Retrieval. Santiago, Chile, 2015:403-412.
[22] 李全, 许新华, 刘兴红, 等. 融合时空感知GRU和注意力的下一个地点推荐[J]. 计算机应用, 2020, 40(3):677-682
LI Quan, XU Xinhua, LIU Xinghong, et al. Next location recommendation based on spatiotemporal-aware GRU and attention[J]. Journal of computer applications, 2020, 40(3):677-682

相似文献/References:

[1]黄立威,李德毅.社交媒体中的信息推荐[J].智能系统学报,2012,7(01):1.
 HUANG Liwei,LI Deyi.A review of information recommendation in social media[J].CAAI Transactions on Intelligent Systems,2012,7(2):1.
[2]郭少成,陈松灿.稀疏化的因子分解机[J].智能系统学报,2017,12(06):816.[doi:10.11992/tis.201706030]
 GUO Shaocheng,CHEN Songcan.Sparsified factorization machine[J].CAAI Transactions on Intelligent Systems,2017,12(2):816.[doi:10.11992/tis.201706030]
[3]马钰,张岩,王宏志,等.面对智能导诊的个性化推荐算法[J].智能系统学报,2018,13(03):352.[doi:10.11992/tis.201711036]
 MA Yu,ZHANG Yan,WANG Hongzhi,et al.A personalized recommendation algorithm for intelligent guidance[J].CAAI Transactions on Intelligent Systems,2018,13(2):352.[doi:10.11992/tis.201711036]
[4]常亮,张伟涛,古天龙,等.知识图谱的推荐系统综述[J].智能系统学报,2019,14(2):207.[doi:10.11992/tis.201805001]
 CHANG Liang,ZHANG Weitao,GU Tianlong,et al.Review of recommendation systems based on knowledge graph[J].CAAI Transactions on Intelligent Systems,2019,14(2):207.[doi:10.11992/tis.201805001]
[5]吴国栋,查志康,涂立静,等.图神经网络推荐研究进展[J].智能系统学报,2020,15(1):14.[doi:10.11992/tis.201908034]
 WU Guodong,ZHA Zhikang,TU Lijing,et al.Research advances in graph neural network recommendation[J].CAAI Transactions on Intelligent Systems,2020,15(2):14.[doi:10.11992/tis.201908034]
[6]梁丽君,李业刚,张娜娜,等.融合用户特征优化聚类的协同过滤算法[J].智能系统学报,2020,15(6):1091.[doi:10.11992/tis.201710024]
 LIANG Lijun,LI Yegang,ZHANG Nana,et al.Collaborative filtering algorithm combining user features and preferences in optimized clustering[J].CAAI Transactions on Intelligent Systems,2020,15(2):1091.[doi:10.11992/tis.201710024]
[7]王健宗,肖京,朱星华,等.联邦推荐系统的协同过滤冷启动解决方法[J].智能系统学报,2021,16(1):178.[doi:10.11992/tis.202009032]
 WANG Jianzong,XIAO Jing,ZHU Xinghua,et al.Cold starts in collaborative filtering for federated recommender systems[J].CAAI Transactions on Intelligent Systems,2021,16(2):178.[doi:10.11992/tis.202009032]

备注/Memo

备注/Memo:
收稿日期:2020-05-21。
作者简介:鲍维克,硕士研究生,主要研究方向为推荐系统;袁春,副研究员,博士,博士生导师,IEEE高级会员,清华大学?香港中文大学媒体科学、技术与系统联合研究中心常务副主任,主要研究方向为机器学习、计算机视觉。发表学术论文100余篇
通讯作者:袁春.E-mail:yuanc@sz.tsinghua.edu.cn
更新日期/Last Update: 2021-04-25