基于知识增强对比学习的长尾用户序列推荐算法

任永功; 周平磊; 张志鹏

doi:10.11959/j.issn.1000-436x.2024107

您当前的位置：

首页 >

文章列表页 >

基于知识增强对比学习的长尾用户序列推荐算法

学术论文 | 更新时间：2024-08-08

- 基于知识增强对比学习的长尾用户序列推荐算法
- Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning
- 通信学报 2024年45卷第6期页码：210-222
- 作者机构：
  
  辽宁师范大学计算机与人工智能学院，辽宁大连 116029
- 作者简介：
  
  [ "任永功（1972- ），男，辽宁兴城人，博士，辽宁师范大学教授，主要研究方向为人工智能、数据挖掘、推荐系统。" ]
  [ "周平磊（1998- ），男，辽宁大连人，辽宁师范大学硕士生，主要研究方向为数据挖掘、推荐系统。" ]
  [ "张志鹏（1988- ），男，河南安阳人，博士，辽宁师范大学讲师，主要研究方向为人工智能、数据挖掘、推荐系统。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61976109);辽宁省“兴辽英才计划”基金资助项目(XLYC2006005);辽宁省高等学校科学研究基金资助项目(LJKZ0963);辽宁省科技厅重点研发基金资助项目(2022JH2/101300271);辽宁省教育厅校基本科研基金资助项目(LJKQZ20222431);教育部产学合作协同育人基金资助项目(202102550005);辽宁省属本科高校基本科研基金资助项目(LS2024Q007)
- DOI：10.11959/j.issn.1000-436x.2024107
  中图分类号： TP391
- 收稿：2024-01-11，
  
  修回：2024-05-15，
  
  纸质出版：2024-06-25
- 稿件说明：
移动端阅览
任永功,周平磊,张志鹏.基于知识增强对比学习的长尾用户序列推荐算法[J].通信学报,2024,45(06):210-222.

REN Yonggong,ZHOU Pinglei,ZHANG Zhipeng.Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning[J].Journal on Communications,2024,45(06):210-222.
任永功,周平磊,张志鹏.基于知识增强对比学习的长尾用户序列推荐算法[J].通信学报,2024,45(06):210-222. DOI： 10.11959/j.issn.1000-436x.2024107.

REN Yonggong,ZHOU Pinglei,ZHANG Zhipeng.Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning[J].Journal on Communications,2024,45(06):210-222. DOI： 10.11959/j.issn.1000-436x.2024107.

摘要

序列推荐根据目标用户的历史交互序列，预测其可能感兴趣的下一个物品。现有的序列推荐方法虽然可以有效捕获用户的历史交互序列中的长期依赖关系，但是无法为交互序列较短且用户数量庞大的长尾用户提供精确推荐。为了解决此问题，提出了一种基于知识增强对比学习的长尾用户序列推荐算法。首先，基于知识图谱中的丰富实体关系信息，构建一个基于语义的物品相似度度量，分别提取原始序列中物品的协同关联物品。然后，基于不同学习序列提出2种序列增强算子，通过增强自监督信号解决长尾用户序列训练数据不足的问题。最后，通过对比自监督任务和推荐主任务的网络参数共享的联合训练，为长尾用户提供更精确的序列推荐结果。在实际数据集上的实验结果表明，所提算法可以有效提高针对长尾用户的序列推荐精度。

Abstract

Sequential recommendation predicts next items for users based on their historical interactions. Existing methods capture long-term dependencies but struggle to recommend precisely for users with short interaction sequences

especially for long-tail users. Therefore

a sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning was proposed. Firstly

semantic item similarity was introduced by leveraging relationships between entities in the knowledge graph to extract correlated items from original sequences. Secondly

two sequence augmentation operators were proposed based on different contrastive learning views

addressing the problem of insufficient training for long-tail user sequences by augmenting self-supervised signals. Finally

precise sequence recommendations were provided for long-tail users by utilizing the joint training of shared network parameters between contrastive self-supervised tasks and the recommendation task. Experimental results on real-world datasets demonstrate the effectiveness of the proposed algorithm in improving performance for long-tail users.

关键词

Keywords

references

HIDASI B , QUADRANA M , KARATZOGLOU A , et al . Parallel recurrent neural network architectures for feature-rich session-based recommendations [C ] // Proceedings of the 10th ACM Conference on Recommender Systems . New York : ACM Press , 2016 : 241 - 248 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Proceedings of the 31st International Conference on Neural Information Processing Systems . New York : ACM Press , 2017 : 6000 - 6010 .

SUN F , LIU J , WU J , et al . BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer [C ] // Proceedings of the Proceedings of the 28th ACM International Conference on Information and Knowledge Management . New York : ACM Press , 2019 : 1441 - 1450 .

MCAULEY J , TARGETT C , SHI Q F , et al . Image-based recommendations on styles and substitutes [C ] // Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM Press , 2015 : 43 - 52 .

LIU Z W , FAN Z W , WANG Y , et al . Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer [C ] // Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM Press , 2021 : 1608 - 1612 .

YIN J W , LIU C H , WANG W Q , et al . Learning transferrable parameters for long-tailed sequential user behavior modeling [C ] // Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . New York : ACM Press , 2020 : 359 - 367 .

JAISWAL A , BABU A R , ZADEH M Z , et al . A survey on contrastive self-supervised learning [J ] . Technologies , 2020 , 9 ( 1 ): 2 - 22 .

张天成 , 田雪 , 孙相会 , 等 . 知识图谱嵌入技术研究综述 [J ] . 软件学报 , 2023 , 34 ( 1 ): 277 - 311 .

ZHANG T C , TIAN X , SUN X H , et al . Overview on knowledge graph embedding technology research [J ] . Journal of Software , 2023 , 34 ( 1 ): 277 - 311 .

SHANI G , HECKERMAN D , BRAFMAN R I , et al . An MDP-based recommender system [J ] . Journal of Machine Learning Research , 2005 , 6 ( 9 ): 1265 - 1295 .

HIDASI B , KARATZOGLOU A . Recurrent neural networks with top-k gains for session-based recommendations [C ] // Proceedings of the 27th ACM International Conference on Information and Knowledge Management . New York : ACM Press , 2018 : 843 - 852 .

KANG W C , MCAULEY J . Self-attentive sequential recommendation [C ] // Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM) . Piscataway : IEEE Press , 2018 : 197 - 206 .

LIU S Y , ZHENG Y J . Long-tail session-based recommendation [C ] // Proceedings of the 14th ACM Conference on Recommender Systems . New York : ACM Press , 2020 : 509 - 514 .

HU L , CAO L B , CAO J , et al . Improving the quality of recommendations for users and items in the tail of distribution [J ] . ACM Transactions on Information Systems , 2017 , 35 ( 3 ): 1 - 37 .

WANG Y , ZHANG H R , LIU Z W , et al . ContrastVAE: contrastive variational autoencoder for sequential recommendation [J ] . arXiv Preprint , arXiv: 2209.00456 , 2022 .

WU J C , WANG X , FENG F L , et al . Self-supervised graph learning for recommendation [C ] // Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM Press , 2021 : 726 - 735 .

YAO T S , YI X Y , CHENG D Z , et al . Self-supervised learning for large-scale item recommendations [C ] // Proceedings of the 30th ACM International Conference on Information & Knowledge Management . New York : ACM Press , 2021 : 4321 - 4330 .

ZHOU K , WANG H , ZHAO W X , et al . S 3 -rec: self-supervised learning for sequential recommendation with mutual information maximization [C ] // Proceedings of the 29th ACM International Conference on Information & Knowledge Management . New York : ACM Press , 2020 : 1893 - 1902 .

XIE X , SUN F , LIU Z Y , et al . Contrastive learning for sequential recommendation [C ] // Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE) . Piscataway : IEEE Press , 2022 : 1259 - 1273 .

MALKOV Y A , YASHUNIN D A . Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020 , 42 ( 4 ): 824 - 836 .

LIU Z W , CHEN Y J , LI J , et al . Contrastive self-supervised sequential recommendation with robust augmentation [J ] . arXiv Preprint , arXiv: 2108.06479 , 2021 .

BASTANLAR Y , ORHAN S . Self-supervised contrastive representation learning in computer vision [M ] . London : IntechOpen , 2022 .

ZHAO W X , HE G L , YANG K L , et al . KB4Rec: a data set for linking knowledge bases with recommender systems [J ] . Data Intelligence , 2019 , 1 ( 2 ): 121 - 136 .

RENDLE S , FREUDENTHALER C , GANTNER Z , et al . BPR: Bayesian personalized ranking from implicit feedback [C ] // Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence . New York : ACM Press , 2009 : 452 - 461 .

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 . New York : ACM Press , 2017 : 173 - 182 .

KARYPIS G . Evaluation of item-based top- N recommendation algorithms [C ] // Proceedings of the Tenth International Conference on Information and Knowledge Management . New York : ACM Press , 2001 : 247 - 254 .

JÄRVELIN K , KEKÄLÄINEN J . IR evaluation methods for retrieving highly relevant documents [J ] . ACM SIGIR Forum , 2017 , 51 ( 2 ): 243 - 250 .

LINDEN G , SMITH B , YORK J . Amazon.com recommendations: item-to-item collaborative filtering [J ] . IEEE Internet Computing , 2003 , 7 ( 1 ): 76 - 80 .

浏览量

1182

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

大语言模型赋能的网络配置分析技术研究

面向威胁情报的小样本文档级事件抽取方法

基于对比学习和预训练Transformer的流量隐匿数据检测方法

基于增强负例多粒度区分模型的视频动作识别研究

基于大语言模型的网络威胁情报知识图谱构建技术研究