Utilizing multi-source data in popularity prediction for shop-type recommendation

作者：

Highlights：

• Multi-source information and big data analytics are utilized to study this problem.

• Location profiles are constructed considering internal and external characteristics.

• The location profile and the commercial structure are integrated.

• The principle of CF is introduced to deal with shop-type recommendation problem.

• The performances of the proposed method are validated on a real-world dataset.

摘要

•Multi-source information and big data analytics are utilized to study this problem.•Location profiles are constructed considering internal and external characteristics.•The location profile and the commercial structure are integrated.•The principle of CF is introduced to deal with shop-type recommendation problem.•The performances of the proposed method are validated on a real-world dataset.

论文关键词：Multi-source data,Shop-type recommendation,Collaborative filtering,Similarity measurement

论文评审过程：Received 26 July 2018, Revised 5 October 2018, Accepted 26 November 2018, Available online 29 November 2018, Version of Record 7 January 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2018.11.033