Enhancing long tail item recommendation in collaborative filtering: An econophysics-inspired approach
作者：
Highlights：
• A novel viewpoint of econophysics principles is formulated to address long tail effect.
• Rating injection strategy inspired from econophysics principles is proposed.
• Performance of the proposed method is evaluated on long tail and non-long tail items.
• Proposed approach immensely improved the number of long tail items in recommendations.
• The proposed principles can be applied as an add-on to state-of-the-art recommendation technique.
摘要
•A novel viewpoint of econophysics principles is formulated to address long tail effect.•Rating injection strategy inspired from econophysics principles is proposed.•Performance of the proposed method is evaluated on long tail and non-long tail items.•Proposed approach immensely improved the number of long tail items in recommendations.•The proposed principles can be applied as an add-on to state-of-the-art recommendation technique.
论文关键词：Long tail items,Collaborative filtering,Power-law distribution,Econophysics,Rating injection
论文评审过程：Received 28 September 2020, Revised 3 September 2021, Accepted 8 September 2021, Available online 15 September 2021, Version of Record 29 September 2021.
论文官网地址：https://doi.org/10.1016/j.elerap.2021.101089
