计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (5): 45-53.doi: 10.11896/jsjkx.230200049

• 数据库&大数据&数据科学 • 上一篇    下一篇

融入时间信息的预训练序列推荐方法

陈稳中1, 陈红梅1,2, 周丽华1,2, 方圆3   

  1. 1 云南大学信息学院 昆明 650500
    2 云南大学云南省智能系统与计算重点实验室 昆明 650500
    3 云南大学西南天文研究所 昆明 650500
  • 收稿日期:2023-02-08 修回日期:2023-05-24 出版日期:2024-05-15 发布日期:2024-05-08
  • 通讯作者: 陈红梅(hmchen@ynu.edu.cn)
  • 作者简介:(1040490734@qq.com)
  • 基金资助:
    国家自然科学基金(62266050,62276227);云南省中青年学术和技术带头人后备人才项目(202205AC160033);云南省基础研究计划重点项目(202201AS070015);云南省智能系统与计算重点实验室开放课题(ISC22Z02)

Time-aware Pre-training Method for Sequence Recommendation

CHEN Wenzhong1, CHEN Hongmei1,2, ZHOU Lihua1,2, FANG Yuan3   

  1. 1 School of Information Science and Engineering,Yunnan University,Kunming 650500,China
    2 Yunnan Key Laboratory of Intelligent Systems and Computing,Yunnan University,Kunming 650500,China
    3 South-Western Institute for Astronomy Research,Yunnan University,Kunming 650500,China
  • Received:2023-02-08 Revised:2023-05-24 Online:2024-05-15 Published:2024-05-08
  • About author:CHEN Wenzhong,born in 1994,postgraduate,is a member of CCF(No.J6048G).His main research interests include recommendation system and heterogeneous information network analysis.
    CHEN Hongmei,born in 1976,Ph.D,associate professor,is a member of CCF(No.49450M).Her main research interests include spatial data mining and location-based social network analysis.
  • Supported by:
    National Natural Science Foundation of China(62266050,62276227),Program for Young and Middle-aged Academic and Technical Reserve Leaders of Yunnan Province(202205AC160033),Yunnan Fundamental Research Projects(202201AS070015) and Open Project Program of Yunnan Key Laboratory of Intelligent Systems and Computing(ISC22Z02).

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摘要： 序列推荐旨在根据用户与项目的历史交互序列,学习用户动态偏好,为用户推荐后续可能感兴趣的项目。基于预训练模型在适应下游任务方面具有优势,预训练机制在序列推荐中备受关注。现有序列推荐预训练方法忽略了现实中时间对用户交互行为的影响,为了更好地捕获用户与项目交互的时间语义,提出了融入时间信息的预训练序列推荐模型TPTS-Rec(Time-aware Pre-Training method for Sequence Recommendation)。首先,在嵌入层引入时间嵌入矩阵以获取用户交互项目与时间的关联信息。然后,在自注意力层采用同一时间点采样的方法以学习项目间的时间关联信息。最后,在微调阶段从时间维度扩增用户交互序列长度以缓解数据稀疏性问题。在真实数据集上的对比实验结果表明,与基线模型相比,所提模型TPTS-Rec的推荐效果有显著提升。

关键词: 序列推荐, 预训练, 自监督学习, 互信息最大化, 时间属性

Abstract: Sequence recommendation aims to learn users' dynamic preferences and recommend the next items which users may be interested in by analyzing historical interaction sequences between users and items.The pre-training model has attracted attentions from researchers in sequence recommendation due to its advantage of being adapted for downstream tasks.The existing pre-training methods for sequence recommendation ignore the impact of time on user interaction behaviors in real life.To better capture the time semantics of interactions between users and items,this paper proposes a novel model TPTS-Rec(time-aware pre-training method for sequence recommendation).First,the time embedding matrix is introduced in the embedding layer to obtain the correlations between items and time in user interaction sequences.Then,the same time sampling method is presented in the self-attention layer to learn the time correlations between items.Finally,in the fine-tuning stage,user interaction sequences are amplified from the time dimension to alleviate the data sparsity.Experiment results on real datasets show that the proposed TPTS-Rec model outperforms the baseline models.

Key words: Sequence recommendation, Pre-training, Self-supervised learning, Mutual information maximization, Time attribute

中图分类号: 

  • TP391
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