数据增强的多模式时间感知序列推荐

李家乐; 王瑞琴; 于洋

doi:10.11959/j.issn.1000-0801.2024234

您当前的位置：

首页 >

文章列表页 >

数据增强的多模式时间感知序列推荐

研究与开发 | 更新时间：2025-03-17

- 数据增强的多模式时间感知序列推荐
- Multi-pattern time-aware sequential recommendation with data augmentation
- 电信科学 2024年40卷第11期页码：66-78
- 作者机构：
  
  湖州师范学院信息工程学院，浙江湖州 313000
- 作者简介：
  
  [ "李家乐（1998- ），男，湖州师范学院信息工程学院硕士生，主要研究方向为数据挖掘、社交网络分析、个性化推荐。" ]
  [ "王瑞琴（1979- ），女，博士，湖州师范学院信息工程学院教授，主要研究方向为自然语言处理、社交网络分析、个性化推荐。" ]
  [ "于洋（1999- ），男，湖州师范学院信息工程学院硕士生，主要研究方向为数据挖掘、自然语言处理、个性化推荐。" ]
- 基金信息：
  
  国家自然科学基金面上项目(62277016)
- DOI：10.11959/j.issn.1000-0801.2024234
  中图分类号： TP391
- 收稿日期：2024-07-18，
  
  修回日期：2024-10-13，
  
  纸质出版日期：2024-11-20
- 稿件说明：
移动端阅览
李家乐,王瑞琴,于洋.数据增强的多模式时间感知序列推荐[J].电信科学,2024,40(11):66-78.

LI Jiale,WANG Ruiqin,YU Yang.Multi-pattern time-aware sequential recommendation with data augmentation[J].Telecommunications Science,2024,40(11):66-78.
李家乐,王瑞琴,于洋.数据增强的多模式时间感知序列推荐[J].电信科学,2024,40(11):66-78. DOI： 10.11959/j.issn.1000-0801.2024234.

LI Jiale,WANG Ruiqin,YU Yang.Multi-pattern time-aware sequential recommendation with data augmentation[J].Telecommunications Science,2024,40(11):66-78. DOI： 10.11959/j.issn.1000-0801.2024234.

摘要

序列推荐系统以包含了显式信息的用户交互序列作为上下文，推测用户的下一个可能动作。其中，时间感知序列推荐挖掘了序列中的时间信息，并考虑了时间信息对用户决策的影响。但是现有的时间感知序列推荐模型只运用到了原始时间信息，原本的序列中还有很多额外信息没有被充分挖掘，如用户评分、项目属性、项目流行度以及项目的标题和评论等文本信息。因此，提出了DMTiSASRec模型，它既可以以更高效的方式挖掘时间信息中的相关秩序，还利用对比学习、多模态等技术对不同的额外信息进行挖掘。在5个不同领域、不同规模的公开数据集的实验数据表明，DMTiSASRec比现有模型更有效。

Abstract

In sequential recommendation systems

explicit user interaction sequences are used as context to infer the user's next possible action. Time-aware sequential recommendation models explore the temporal information within the sequence and consider the impact of time on user decisions. However

existing time-aware sequential recommendation models only utilize raw temporal information

while many additional pieces of information in the original sequence are not fully exploited

such as user ratings

item attributes

item popularity

and textual information like item titles and reviews. Therefore

the DMTiSASRec model was proposed

which not only efficiently extracted relevant orders beyond temporal information but also leveraged techniques like contrastive learning and multi-modal methods to mine different types of additional information. Experiments on five publicly available datasets across different domains and scales show that DMTiSASRec outperforms existing models in terms of effectiveness.

关键词

Keywords

references

RENDLE S , FREUDENTHALER C , SCHMIDT L . Factorizing personalized Markov chains for next-basket recommendation [C ] // Proceedings of the 19th International Conference on World Wide Web . New York : ACM Press , 2010 : 11 - 820 .

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 .

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 : Curran Associates Inc , 2017 : 6000 - 6010 .

KANG W , MCAULEY J . Self-attentive sequential recommendation [C ] // Proceeding of the 18th IEEE International Conference on Data Mining . Singapore : IEEE Press , 2018 : 197 - 206 .

黄熠旻 , 纪其顺 , 万超艺 , 等 . 注意力感知的边-节点交换图神经网络模型 [J ] . 电信科学 , 2024 , 40 ( 1 ): 106 - 114 .

HUANG Y M , JI Q S , WAN C Y , et al . Attention aware edge-node exchange graph neural network [J ] . Telecommunications Science , 2024 , 40 ( 1 ): 106 - 114 .

杨帅 , 王瑞琴 , 马辉 . 基于多通道的边学习图卷积网络 [J ] . 电信科学 , 2022 , 38 ( 9 ): 95 - 104 .

YANG S , WANG R Q , MA H . Multi-channel based edge-learning graph convolutional network [J ] . Telecommunications Science , 2022 , 38 ( 9 ): 95 - 104 .

陶涛 , 李珍 , 王冀彬 , 等 . 基于图神经网络的权益推荐技术方案研究 [J ] . 电信科学 , 2023 , 39 ( 8 ): 91 - 101 .

TAO T , LI Z , WANG J B , et al . Research on the graphical convolution neural network based benefits recommendation system strategy [J ] . Telecommunications Science , 2023 , 39 ( 8 ): 91 - 101 .

LI J , WANG Y , MCAULEY J . Time interval aware self-attention for sequential recommendation [C ] // Proceedings of the 13th International Conference on Web Search and Data Mining . New York : ACM Press , 2020 : 322 - 330 .

CHEN R , LIANG G , MA C , et al . HTP: exploiting holistic temporal patterns for sequential recommendation [C ] // Proceedings of the 2023 International Joint Conference on Neural Networks . Queensland, Piscataway : IEEE Press , 2023 : 1 - 8 .

ZHANG Y , YIN G , DONG Y , et al . Contrastive learning with frequency domain for sequential recommendation [J ] . Applied Soft Computing , 2023 , 144 ( C ): 110481 - 110494 .

LI Y , DU H , NI Y , et al . Multi-modality is all you need for transferable recommender systems [J ] . arXiv preprint , 2023 , arXiv: 2312. 09602 .

ZHU Y , LI H , LIAO Y , et al . What to do next: modeling user behaviors by time-LSTM [C ] // Proceedings of the 26th International Joint Conference on Artificial Intelligence . Melbourne, Australia : AAAI Press , 2017 : 3602 – 3608 .

FAN Z , LIU Z , ZHANG J , et al . Continuous-time sequential recommendation with temporal graph collaborative [C ] // Proceedings of the 30th ACM International Conference on Information and Knowledge Management . New York : ACM Press , 2021 : 433 - 442 .

JUNSU C , DONGMIN H , SEONGKU K , et al . Learning heterogeneous temporal patterns of user preference for timely recommendation [C ] // Proceedings of the 30th Web Conference . New York : ACM Press , 2021 : 1274 – 1283 .

CHEN M , PENG K , XUE L . Hierarchical gating networks for sequential recommendation [C ] // Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19) . New York : ACM Press , 2019 : 825 – 833 .

SHI C Y , REN P J , FU D J , et al . Diversifying sequential recommendation with retrospective and prospective transformers [J ] . ACM Trans. Inf. Syst. 2024 , 42 ( 5 ): 37 - 41 .

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 (SIGIR’21) . New York : ACM Press , 2021 : 1608 – 1612 .

WEI W , HUANG C , XIA L H , et al . Multi-modal self-supervised learning for recommendation [C ] // Proceedings of the ACM Web Conference 2023 (WWW '23) . New York : ACM Press , 2023 : 790 – 800 .

CHEN T , SIMON K , MOHAMMAD N , et al . A simple framework for contrastive learning of visual representations [C ] // Proceedings of the 37th International Conference on Machine Learning . Online : JML R.org , 2020 : 1597 - 1607 .

ZHOU C , MA J , ZHANG J , et al . Contrastive learning for debiased candidate generation in large-scale recommender systems [C ] // Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . New York : ACM Press , 2021 : 3985 – 3995 .

XIE X , SUN F , LIU Z , et al . Contrastive pre-training for sequential recommendation [J ] . arXiv preprint , 2020 , arXiv: 2010.14395 .

TANG J , WANG K . Personalized Top-N sequential recommendation via convolutional sequence embedding [C ] // Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . Marina Del Rey : ACM Press , 2018 : 565 – 573 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于对比学习的社交媒体地理位置预测方法