摘要
在以往基于自注意力机制的序列推荐模型中,由于忽略上下文信息和长期行为信息,常导致模型无法捕捉到多层次的序列特征.因此提出一种融合时间上下文与长短期偏好的序列推荐模型.首先利用感知时间间隔的自注意力机制来建模用户和项目短期行为交互的关系模式,然后补充用户长期偏好来增强用户特征表示,并利用门控机制将长短期偏好表示进行融合生成用户最终兴趣表示.最后对用户交互项目间的共现模式进行建模,并结合用户最终兴趣表示进行项目推荐.在3个公开数据集上进行实验的结果验证了该模型的有效性.
The previous sequence recommendation models based on self-attention mechanism can not capture multi-level sequence features due to the neglect of context information and long-term behavior information.Therefore,a sequence recommendation model combining time context with short-term preference is proposed.Firstly,the self attention mechanism of perceived time interval is used to model the relationship mode of recent interaction between users and projects.Then the user′s long-term preference is supplemented to enhance the user′s feature representation.And the gating mechanism is used to fuse the long-term and short-term preference representations to generate the user′s final interest representation.Finally,the project recommendation is carried out by combining the co-occurrence mode between user interaction projects with the final expression of user′s final interest representation.The experimental results on three public data sets demonstrate the effectiveness of the proposed model.
作者
胡胜利
林凯
HU Shengli;LIN Kai(School of Computer Science and Engineering,Anhui University of Science and Technology,Huainan 232001,China)
出处
《湖北民族大学学报(自然科学版)》
CAS
2022年第3期328-335,共8页
Journal of Hubei Minzu University:Natural Science Edition
基金
安徽理工大学2020年研究生创新基金项目(2020CX2071)。
关键词
序列推荐
时间信息
项目共现
上下文信息
长短期偏好
sequence recommendation
time information
project co-occurrence
context information
long-short term preferences