计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 47-55.doi: 10.11896/jsjkx.220100264

• 数据库&大数据&数据科学 • 上一篇    下一篇


雒晓辉1,2, 吴云1,2, 王晨星1, 余文婷1   

  1. 1 贵州大学公共大数据国家重点实验室 贵阳 550025
    2 贵州大学计算机科学与技术学院 贵阳 550025
  • 收稿日期:2022-01-27 修回日期:2022-06-22 出版日期:2023-04-15 发布日期:2023-04-06
  • 通讯作者: 吴云(wuyun_v@126.com)
  • 作者简介:(gs.xhluo20@gzu.edu.cn)
  • 基金资助:

Sequential Recommendation Model Based on User’s Long and Short Term Preference

LUO Xiaohui1,2, WU Yun1,2, WANG Chenxing1, YU Wenting1   

  1. 1 State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China
    2 College of Computer Science and Technology,Guizhou University,Guiyang 550025,China
  • Received:2022-01-27 Revised:2022-06-22 Online:2023-04-15 Published:2023-04-06
  • About author:LUO Xiaohui,born in 1998,postgra-duate,is a student member of China Computer Federation.His main research interests include artificial intelligence and big data,data mining and recommendation system.
    WU Yun,born in 1973,Ph.D,associate professor,is a senior member of China Computer Federation.His main research interests include artificial intelligence,computer vision,deep learning and recommendation system.
  • Supported by:
    Science and Technology Foundation of Guizhou Province(ZK[2022]119) and National Natural Science Foundation of China(61662009).

摘要: 针对现有序列推荐模型忽略了不同用户的个性化行为,导致模型不能充分捕获用户动态偏好而产生的兴趣漂移等问题,提出了一种基于用户长短期偏好的序列推荐模型(Sequential Recommendation Model Based on User’s Long and Short Term Preference,ULSP-SRM)。首先,根据用户的序列中交互物品的类别和时间信息生成用户的动态类别嵌入,进而有效建立物品之间的关联性,并且降低数据的稀疏性;其次,根据用户当前点击物品和最后一项点击的时间间隔信息生成个性化时序位置嵌入矩阵,模拟用户的个性化聚集现象,以更好地反映用户偏好的动态变化;然后,将融合了个性化时序位置嵌入矩阵的用户长期偏好序列以会话为单位输入门控循环单元中,生成用户的长期偏好表示,并通过注意力机制将用户长短期偏好进行融合,生成用户的最终偏好表示,从而达到充分捕获用户偏好的目的;最后,将用户最终偏好表示输入推荐预测层进行下一项推荐预测。在Amazon公开数据集的7个子集上进行实验,采用AUC(Area Under Curve)值、召回率和精确率指标进行综合评估,实验结果表明,所提模型的表现优于其他先进基准模型,有效地提升了推荐性能。

关键词: 序列推荐, 长短期偏好, 个性化时序位置, 兴趣漂移, 注意力机制

Abstract: Aiming at the problem that the existing sequence recommendation model ignores the personalized behavior of different users,the model cannot fully capture the interest drift caused by users’ dynamic preferences,a sequence recommendation model based on users’ long and short term preferences(ULSP-SRM)is proposed.Firstly,the dynamic category embedding of the user is generated according to the category and time information of the interactive items in the user’s sequence,thereby effectively establishing the correlation between the items and reducing the sparsity of the data.Secondly,according to the time interval information of the user’s current clicked item and the last clicked item,a personalized time series position embedding matrix is generated to simulate the user’s personalized aggregation phenomenon and better reflect the dynamic change of user preference.Then,the user’s long-term preference sequence fused with the personalized time-series position embedding matrix is input into the gated recurrent unit in units of sessions to generate the user’s long-term preference representation,and the user’s long and short term preferences are fused through the attention mechanism to generate the final preference representation of the user,to achieve the purpose of fully capturing the user’s preference.Finally,the final user preference representation is input to the recommendation prediction layer for the next recommendation prediction.Experiments are carried out on seven subsets of Amazon public data set,and the area under curve(AUC ),recall rate and precision rate indicators are used for comprehensive evaluation.Experimental results show that the proposed model outperforms other advanced benchmark models,effectively improving recommended perfor-mance.

Key words: Sequence recommendation, Long and short term preference, Personalized time series location, Interest in the drift, Attention mechanism


  • TP311
[1]WANG S,CAO L,WANG Y,et al.A survey on session-based recommender systems[J].ACM Computing Surveys(CSUR),2021,54(7):1-38.
[2]PÉREZ-ALMAGUER Y,YERA R,ALZAHRANI A A,et al.Content-based group recommender systems:A general taxonomy and further improvements[J].Expert Systems with Applications,2021,184:115444.
[3]CHEN X,XU H,ZHANG Y,et al.Sequential recommendationwith user memory networks[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mi-ning.2018:108-116.
[4]WANG N,HE X M,LIU Z Q,et al.A personalized video recommendation strategy based on user playing behavior sequences[J].Chinese Journal of Computers,2020,43(1):123-135.
[5]CHEN J P,HU H L,ZHANG F,et al.Convolutional sequential recommendation with temporal feature and user preference[J].Computer Science,2022,49(1):115-120.
[6]RENDLE S,FREUDENTHALER C,SCHMIDT-THIEME L.Factorizing personalized markov chains for next-basket recommendation[C]//Proceedings of the 19th International Confe-rence on World Wide Web.2010:811-820.
[7]HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Session-based recommendations with recurrent neural networks[J].arXiv:1511.06939,2015.
[8]LI J,REN P,CHEN Z,et al.Neural attentive session-based re-commendation[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.2017:1419-1428.
[9]LIU Q,ZENG Y,MOKHOSI R,et al.STAMP:short-term attention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2018:1831-1839.
[10]WANG M,REN P,MEI L,et al.A collaborative session-based recommendation approach with parallel memory modules[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:345-354.
[11]XING C Z,ZHU JI X,MENG X F,et al.Research review on recommendation methods for points of interest[J].Computer Science,2021,48(11A):176-183.
[12]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008.
[13]HUANG X,QIAN S,FANG Q,et al.Csan:Contextual self-attention network for user sequential recommendation[C]//Proceedings of the 26th ACM International Conference on Multimedia.2018:447-455.
[14]YING H,ZHUANG F,ZHANG F,et al.Sequential recommender system based on hierarchical attention network[C]//IJCAI International Joint Conference on Artificial Intelligence.2018:3926-3932.
[15]GRBOVIC M,CHENG H.Real-time personalization using embeddings for search ranking at airbnb[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2018:311-320.
[16]CHEN J,WANG X,ZHAO S,et al.Deep attention user-based collaborative filtering for recommendation[J].Neurocomputing,2020,383(C):57-68.
[17]KOOHI H,KIANI K.User based collaborative filtering using fuzzy c-means[J].Measurement,2016,91:134-139.
[18]BELLOG A,PARAPAR J.Using graph partitioning techniques for neighbour selection in user-based collaborative filtering[C]//Proceedings of the Sixth ACM Conference on Recommender Systems.2012:213-216.
[19]TIAN Z,PAN L M,YIN P,et al.Recommendation algorithm for deep matrix factorization[J].Journal of Software,2021,32(12):3917-3928.
[20]QIAN X,FENG H,ZHAO G,et al.Personalized recommendation combining user interest and social circle[J].IEEE Transactions on Knowledge and Data Engineering,2013,26(7):1763-1777.
[21]CAI H N,NIU B H,WEN J H,et al.Recommendation algo-rithm based on time series model and matrix factorization[J].Computer Application Research,2018,35(6):1624-1627.
[22]WANG N,HE X M,LIU Z Q,et al.A personalized video recommendation strategy based on user playing behavior sequences[J].Journal of Computers,2020,43(1):123-135.
[23]LIU Q,WU S,WANG L.Multi-behavioral sequential prediction with recurrent log-bilinear model[J].IEEE Transactions on Knowledge and Data Engineering,2017,29(6):1254-1267.
[24]ZHOU M,DING Z,TANG J,et al.Micro behaviors:A new perspective in e-commerce recommender systems[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining.2018:727-735.
[25]KENTON J D M W C,TOUTANOVA L K.Bert:Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL-HLT.2019:4171-4186.
[26]SUN F,LIU J,WU J,et al.BERT4Rec:Sequential recommendation with bidirectional encoder representations from transformer[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.2019:1441-1450.
[27]GRAVES A.Long short-term memory[M]//Supervised Se-quence Labelling with Recurrent Neural Networks.Berlin/Heidelberg:Springer,2012:37-45.
[28]DO A M,RUPERT A V,WOLFORD G.Evaluations of pleasu-rable experiences:The peak-end rule[J].Psychonomic Bulletin &Review,2008,15(1):96-98.
[29]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[J].arXiv:1205.2618,2012.
[30]JOZEFOWICZ R,ZAREMBA W,SUTSKEVER I.An empirical exploration of recurrent network architectures[C]//InternationalConference on Machine Learning.PMLR,2015:2342-2350.
[31]CAO Y,ZHANG W,SONG B,et al.Position-aware context attention for session-based recommendation[J].Neurocomputing,2020,376(C):65-72.
[32]DU Y,LIU H,QU Y,et al.Online personalized next-item re-commendation via long short term preference learning[C]//Pacific Rim International Conference on Artificial Intelligence.Cham:Springer,2018:915-927.
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