[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

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(1):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(6):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(3):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

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
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134