基于辅助信息与长短期偏好的序列推荐  

Sequence recommendation based on side information and long-short term preferences

作　　者：刘超[1] 任梦瑶 冯禄华 Liu Chao;Ren Mengyao;Feng Luhua(College of Computer Science&Engineering,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学计算机科学与工程学院,重庆400054

出　　处：《计算机应用研究》2024年第9期2628-2634,共7页Application Research of Computers

基　　金：重庆市社科联资助项目(2021NDYB101)。

摘　　要：为了解决序列推荐中的用户偏好漂移问题,以及更精确地捕捉用户动态偏好,提出了一种新型的序列推荐模型SILSSRec(side information and long-short term preferences based sequence recommendation)。该模型首先利用项目的类别和频次作为辅助信息,基于用户的历史交互序列,生成个性化用户嵌入表示。然后,通过历史交互和当前交互之间的时间间隔生成个性化时间间隔嵌入,并将此嵌入与项目特征嵌入融合,形成个性化时间嵌入表示。模型采用注意力机制和门控循环网络,从嵌入表示中提取用户的长期和短期偏好。此外,通过对比学习强化偏好的特征表达,并使用自适应聚合网络动态融合这两种偏好,形成用户的最终偏好表示。在8个公开数据集上的实验结果表明,SILSSRec在评估指标上优于现有的基线模型,其中AUC(area under curve)平均提高了3.82%、召回率平均提高了7.2%、精确率平均提高了0.3%。实验证明SILSSRec在不同场景下均有较好表现,有效缓解了偏好漂移问题,提升了推荐效果。To address the issue of user preference drift and capture dynamic user preferences more accurately in sequence re-commendation,this paper proposed a novel model named SILSSRec.The model initially leveraged categories and frequencies of items as side information to generate personalized user embeddings based on users’historical interaction sequences.Then it created personalized temporal interval embeddings by considering the time intervals between historical and current interactions,and integrated these embeddings with item feature embeddings to form personalized temporal embeddings.The model employed attention mechanisms and gated recurrent networks to extract users’long-term and short-term preferences from the embedding representations.Furthermore,it used contrastive learning to reinforce the feature representation of preferences,and an adaptive aggregation network dynamically combined these two types of preferences to form the final preference representation of users.Experiments on eight public datasets demonstrate that SILSSRec outperforms existing baseline models on evaluation metrics,with an average increase of 3.82%in AUC,7.2%in recall rate,and 0.3%in precision.The results validate that SILSSRec performs well in various scenarios,effectively mitigating the preference drift issue and enhancing recommendation performance.

关 键 词：序列推荐 辅助信息 注意力机制 长短期偏好 对比学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]