计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (1): 142-150.doi: 10.11896/jsjkx.240700186

• 数据库&大数据&数据科学 • 上一篇    下一篇

序列标签推荐

刘冰, 徐鹏宇, 陆思进, 王诗菁, 孙宏健, 景丽萍, 于剑   

  1. 北京交通大学交通数据分析与挖掘北京市重点实验 北京 100044
    北京交通大学计算机与信息技术学院 北京 100044
  • 收稿日期:2024-07-29 修回日期:2024-09-23 出版日期:2025-01-15 发布日期:2025-01-09
  • 通讯作者: 景丽萍(lpjing@bjtu.edu.cn)
  • 作者简介:(bing.liu@bjtu.edu.cn)
  • 基金资助:
    中央高校基本科研业务费专项资金(2019JBZ110);国家自然科学基金(62176020);国家重点研发计划(2020AAA0106800);北京市自然科学基金(L211016)

Sequential Tag Recommendation

LIU Bing, XU Pengyu, LU Sijin, WANG Shijing, SUN Hongjian, JING Liping, YU Jian   

  1. Beijing Key Lab of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing 100044,China
    School of Computer Science and Technology,Beijing Jiaotong University,Beijing 100044,China
  • Received:2024-07-29 Revised:2024-09-23 Online:2025-01-15 Published:2025-01-09
  • About author:LIU Bing,born in 2000,master,is a student member of CCF(No.P2313G).Her main research interests include tag recommendation and multi-label lear-ning.
    JING Liping,Ph.D,professor,is a professional member of CCF(No.18443S).Her main research interests include machine learning and its application in artificial intelligence field and so on.
  • Supported by:
    Fundamental Research Funds for the Central Universities(2019JBZ110),National Natural Science Foundation of China(62176020),National Key Research and Development Program of China(2020AAA0106800) and Natural Science Foundation of Beijing,China(L211016).

PDF (PC)

147

摘要： 随着互联网技术的发展以及社交网络的扩大,网络平台已经成为人们获取信息的一个重要途径。标签的引入提升了信息分类及检索效率。同时,标签推荐系统的出现不仅方便了用户输入标签,还提高了标签的质量。传统的标签推荐算法通常只考虑标签和项目两个主体,而忽略了用户在选择标签时个人意图所起到的重要作用。由于在标签推荐系统中标签最终由用户确定,因此用户的偏好在标签推荐中起着关键作用。为此,引入用户作为主体,并结合用户发布的历史帖子的先后顺序,将标签推荐任务建模为更加符合真实场景的序列标签推荐任务。提出了一种基于MLP的序列标签推荐方法(MLP for Sequential Tag Recommendation,MLP4STR),该方法显式地建模用户偏好用于引导整体标签推荐。MLP4STR采用一种跨特征对齐的MLP序列特征提取框架,将文本和标签的特征对齐,获取用户的历史帖子信息和历史标签信息中隐含的用户动态兴趣。最后,结合帖子内容和用户偏好进行标签推荐。在4个真实世界的数据集上得到的实验结果表明,MLP4STR能够有效地学习序列标签推荐中的用户历史行为序列的信息,其中,评价指标F1@5较最优的对比算法有显著提升。

关键词: 标签推荐, 序列推荐, 多标签学习, 用户偏好

Abstract: With the development of Internet technology and the expansion of social networks,online platforms have become a significant avenue for people to access information.The introduction of tags has facilitated the categorization and retrieval of information.At the same time,the advent of tag recommendation systems not only makes it easier for users to input tags but also improves the quality of tags.Traditional tag recommendation algorithms typically only consider tags and items,overlooking the crucial role of personal intent when users choose tags.Since tags in a recommendation system are ultimately determined by users,user preferences play a key role in tag recommendation.Therefore,we introduce the user as a subject,and by incorporating the chronological order of users’ historical posts,modeling the task of tag recommendation as a sequential tag recommendation task that is more aligned with real-world scenarios.To address this task,this paper proposes a method named MLP for sequential tag recommendation(MLP4STR),which explicitly models user preferences to guide the overall tag recommendation.MLP4STR employs a cross-feature alignment MLP framework for sequence feature extraction,aligns the features of text and tags to capture the dynamic interests of users implicit in their historical post and tag information.Finally,it recommends tags by combining post content and user preferences.Experimental results on four real-world datasets show that MLP4STR can effectively learn information from users’ historical behavior sequences in sequential tag recommendation,and the evaluation metric F1@5 shows a significant improvement compared to the optimal baseline algorithms.

Key words: Tag recommendation, Sequential recommendation, Multi-label learning, User preference

中图分类号: 

  • TP391
[1]KRESTEL R,FANKHAUSER P,NEJDL W,et al.Latentdirichlet allocation for tag recommendation[C]//Proceedings of the ACM Conference on Recommender Systems.New York:Association for Computing Machiery,2009:61-68.
[2]BELÉM F M,ALMEIDA J M,GONÇALVES M A.A survey on tag recommendation methods[J].Journal of the Association for Information Science and Technology,2017,68(4):830-844.
[3]XU P Y,LIU H F,LIU B.Survey of Tag RecommendationMethods Journal of Software[J].Journal of Software,2021,33(4):1244-1266.
[4]SUN J S,ZHU M Y,JIANG Y C,et al.Hierarchical attention model for personalized tag recommendation[J].Journal of the Association for Information Science and Technology,2021,72(2):173-189.
[5]SONG Y,ZHUANG Z M,LI H J,et al.Realtime automatic tag recommendation[C]//Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:Association for Computing Machiery,2008:515-522.
[6]XIA X,DAVID L,WANG X Y,et al.Tag recommendation insoftware information sites[C]//Proceedings of the 10th Wor-king Conference on Mining Software Repositories(MSR).Pisca-taway:IEEE Computer Society,2013:287-296.
[7]WU Y,YAO Y,XU F,et al.Tag2word:Using tags to generate words for content based tag recommendation[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management.New York:Association for Computing Machiery,2016:2287-2292.
[8]WU Y,XI S Q,YAO Y,et al.Guiding supervised topic modeling for content based tag recommendation[J].Neurocomputing,2018,314:479-489.
[9]TANG S J,YAO Y,ZHANG S W,et al.An integral tag recommendation model for textual content[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Menlo Park:AAAI Press,2019:5109-5116.
[10]LEI K,FU Q A,YANG M,et al.Tag recommendation by text classification with attention-based capsule network[J].Neurocomputing,2020,391:65-73.
[11]LI Y,LIU T,JIANG J,et al.Hashtag recommendation withtopical attention-based lstm[C]//Proceedings of the 26th International Conference on Computational Linguistics:Technical Papers.New York:Association for Computing Machiery,2016:3019-3029.
[12]HASSAN H A,SANSONETTI G,GASPARETTI F,et al.Semanticbased tag recommendation in scientific bookmarking systems[C]//Proceedings of the 12th ACM Conference on Recommender Systems.New York:Association for Computing Machiery,2018:465-469.
[13]HE J D,XU B W,YANG Z,et al.PTM4Tag:Sharpening Tag Recommendation of Stack Overflow Posts with Pre-trained Models[C]//Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension.New York:Association for Computing Machinery,2022:1-11.
[14]CHEN Y C,LAI K T,LIU D,et al.Tagnet:triplet-attention graph networks for hashtag recommendation[J].IEEE Transactions on Circuits and Systems for Video Technology,2021,32(3):1148-1159.
[15]FENG K,LIU T,ZHANG H,et al.Tnod:Transformer network with object detection for tag recommendation[C]//Proceedings of the 2023 ACM International Conference on Multimedia Retrieval.2023:617-621.
[16]LI L,WANG P,ZHENG X,et al.Dual-interactive fusion for code-mixed deep representation learning in tag recommendation[J].Information Fusion,2023,99:101862.
[17]WANG L,LI Y.KEIC:A tag recommendation framework with knowledge enhancement and interclass correlation[J].Information Sciences,2023,645:119330.
[18]SIGURBJÖRNSSON B,ZWOL R.Flickr tag recommendationbased on collective knowledge[C]//Proceedings of the International Conference on World Wide Web.New York:Springer,2008.327-336.
[19]NGUYEN H,WISTUBA M,GRABOCKA J,et al.Personalized deep learning for tag recommendation[C]//Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mi-ning.Berlin:Springer,2017:186-197.
[20]MAITY S K,PANIGRAHI A,GHOSH S,et al.DeepTagRec:A content-cum-user based tag recommendation framework for stack overflow[C]//Proceedings of European Conference on Information Retrieval.Switzerland,Springer,2019:125-131.
[21]QUINTANILLA E,RAWAT Y,SAKRYUKIN A,et al.Adversarial learning for personalized tag recommendation[C]//IEEE Transactions on Multimedia.Piscataway:IEEE Computer Society,2020,23:1083-1094.
[22]ZHANG S W,YAO Y,XU F,et al.Hashtag recommendationfor photo sharing services[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Menlo Park:AAAI Press,2019:5805-5812.
[23]KANG W C,MCAULEY J.Self-attentive sequential recommendation[C]//2018 IEEE International Conference on Data Mi-ning(ICDM).Piscataway:IEEE Computer Society,2018:197-220.
[24]ZHANG T T,ZHAO P P,LIU Y C,et al.Feature-level deeper self-attention network for sequential recommendation[C]//Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.San Francisco:Morgan Kaufmann,2019:4320-4326.
[25]ZHOU P L,YE Q C,XIE Y Q,et al.Attention Calibration for Transformer-based Sequential Recommendation[C]//Procee-dings of the 32nd ACM International Conference on Information and Knowledge Management.2023:3595-3605.
[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.New York:Association for Computing Machiery,2019:1441-1450.
[27]ZHENG J,RAMASINGHE S,LUCEY S.Rethinking positional encoding[J].arXiv:2107.02561.2021.
[28]LI M,ZHAO X,LYU C,et al.MLP4Rec:A pure MLP architecture for sequential recommendations[C]//Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.San Francisco:Morgan Kaufmann,2022:2138-2144.
[29]EKAMBARAM V,JATI A,NGUYEN N,et al.TSMixer:Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.New York:Association for Computing Machiery,2023:459-469.
[30]DEVLIN J,CHANG MW,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg:Association for Computational Linguistics,2019:4171-4186.
[31]KUMAR S,SHIVANI A,AKHTAR M S,et al.When did you become so smart,oh wise one?Sarcasm Explanation in Multi-modal Multi-party Dialogues[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistic.2022:5956-5968.
[32]LI M Y,ZHANG Z J,ZHAO X Y,et al.AutoMLP:Automated MLP for Sequential Recommendations[C]//Proceedings of the ACM Web Conference.New York:Association for Computing Machiery,2023:1190-1198.
[33]LIANG J H,ZHAO X Y,LI M Y,et al.MMMLP:multi-modal multilayer perceptron for sequential recommendations[C]//Proceedings of the ACM Web Conference 2023.2023:1109-1117.
[34]GONG Y Y,ZHANG Q.Hashtag recommendation using attention-based convolutional neural network[C]//Proceedings of International Joint Conference on Artificial Intelligence.San Francisco:Morgan Kaufmann,2016:2782-2788.
[35]SUN B,ZHU Y Z,XIAO Y K,et al.Automatic Question Tagging with Deep Neural Networks[J].IEEE Transactions on Learning Technologies.Piscataway:IEEE Computer Society,2019:29-43.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发