Temporal Density-aware Sequential Recommendation Networks with Contrastive Learning

作者：

Highlights：

• Integrating temporal kernel density information into sequential recommendation.

• Capturing long and short-term user preference via attention and convolution encoders.

• Deriving auxiliary self-supervision signals without manual data augmentation.

• Extensive experiments with several popular baselines on five real-world datasets.

摘要

•Integrating temporal kernel density information into sequential recommendation.•Capturing long and short-term user preference via attention and convolution encoders.•Deriving auxiliary self-supervision signals without manual data augmentation.•Extensive experiments with several popular baselines on five real-world datasets.

论文关键词：Sequential recommendation,Kernel density estimation,Attention mechanism,Convolutional neural networks,Contrastive learning

论文评审过程：Received 13 May 2022, Revised 4 August 2022, Accepted 12 August 2022, Available online 19 August 2022, Version of Record 27 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118563