Mitigating long tail effect in recommendations using few shot learning technique

作者：

Highlights：

• A novel framework to mitigate long tail effect in recommendations is proposed.

• Limited ratings problem is addressed using few shot learning technique.

• Three evaluation metrics suitable for long tail scenarios are introduced.

• Results on real-world datasets show the effectiveness of the proposed framework.

摘要

•A novel framework to mitigate long tail effect in recommendations is proposed.•Limited ratings problem is addressed using few shot learning technique.•Three evaluation metrics suitable for long tail scenarios are introduced.•Results on real-world datasets show the effectiveness of the proposed framework.

论文关键词：Long tail items,Hierarchical clustering,Few shot learning,Siamese networks

论文评审过程：Received 18 July 2018, Revised 8 July 2019, Accepted 17 August 2019, Available online 19 August 2019, Version of Record 29 August 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112887