Sequencing of items in personalized recommendations using multiple recommendation techniques
作者：
Highlights：
• Proposed approach has high precision value for small top-n recommendations.
• Sequencing of items in recommendation list is made on basis of popularity.
• Both ratings and opinions of users are used about items.
• Handles item side cold start and gray sheep problems in fairly simple manner.
• Experiment shows around 20% improvement in precision for small n values.
摘要
•Proposed approach has high precision value for small top-n recommendations.•Sequencing of items in recommendation list is made on basis of popularity.•Both ratings and opinions of users are used about items.•Handles item side cold start and gray sheep problems in fairly simple manner.•Experiment shows around 20% improvement in precision for small n values.
论文关键词：Recommendation system,Item sequence in recommendation,Content based filtering,Collaborative filtering,Opinion mining,E-commerce.
论文评审过程：Received 8 August 2017, Revised 20 October 2017, Accepted 7 December 2017, Available online 12 December 2017, Version of Record 20 December 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.12.019
