Attention-aware metapath-based network embedding for HIN based recommendation
作者：
Highlights：
• Heterogenous information network based recommendation is investigated.
• An attention-aware metapath-based network embedding approach is proposed.
• Each metapath is modeled as a weighted homogenous information network.
• A self-attention mechanism generates integrated representations of users and items.
• Deep neural network methods are used in the final stage of prediction.
摘要
•Heterogenous information network based recommendation is investigated.•An attention-aware metapath-based network embedding approach is proposed.•Each metapath is modeled as a weighted homogenous information network.•A self-attention mechanism generates integrated representations of users and items.•Deep neural network methods are used in the final stage of prediction.
论文关键词：Heterogeneous information network,Recommender system,Network embedding,Deep learning,Attention mechanism
论文评审过程：Received 22 September 2020, Revised 10 January 2021, Accepted 10 January 2021, Available online 19 January 2021, Version of Record 21 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114601
