Multi-pattern time-aware sequential recommendation with data augmentation

LI Jiale; WANG Ruiqin; YU Yang

doi:10.11959/j.issn.1000-0801.2024234

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Multi-pattern time-aware sequential recommendation with data augmentation

Research and Development | 更新时间：2025-03-17

- Multi-pattern time-aware sequential recommendation with data augmentation
- Telecommunications Science Vol. 40, Issue 11, Pages: 66-78(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62277016)
- DOI：10.11959/j.issn.1000-0801.2024234
  CLC： TP391
- Received：18 July 2024，
  
  Revised：13 October 2024，
  
  Published：20 November 2024
- 稿件说明：
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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.

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references

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