Sequence neural network for recommendation with multi-feature fusion

作者：

Highlights：

• A linear GRU with multi-feature fusion based on contextual information is proposed.

• A new multi-feature user integration GRU to filter user information is proposed.

• The TextCNN is used to handle the diversity of text data in the datasets.

• Ablation studies verify the impact of different features.

• Our methods outperform competitive methods in terms of MRR, Recall and F1.

摘要

•A linear GRU with multi-feature fusion based on contextual information is proposed.•A new multi-feature user integration GRU to filter user information is proposed.•The TextCNN is used to handle the diversity of text data in the datasets.•Ablation studies verify the impact of different features.•Our methods outperform competitive methods in terms of MRR, Recall and F1.

论文关键词：Item recommendation,GRU,Sequence model,Multi-feature fusion

论文评审过程：Received 17 November 2021, Revised 10 June 2022, Accepted 5 August 2022, Available online 11 August 2022, Version of Record 17 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118459