An orthogonal-space-learning-based method for selecting semantically helpful reviews
作者：
Highlights：
• Selecting a helpful review set fits customers’ heterogeneous information demands.
• We conceptualize review set helpfulness into credibility, relevance, diversity, and coverage.
• We propose an orthogonal-semantic-space-based method for helpful review set selection.
• Experiment results on designed metrics validate the helpfulness of the selected review sets.
• Explanatory analysis offer insightful findings regarding the review set helpfulness.
摘要
•Selecting a helpful review set fits customers’ heterogeneous information demands.•We conceptualize review set helpfulness into credibility, relevance, diversity, and coverage.•We propose an orthogonal-semantic-space-based method for helpful review set selection.•Experiment results on designed metrics validate the helpfulness of the selected review sets.•Explanatory analysis offer insightful findings regarding the review set helpfulness.
论文关键词：Helpful review set selection,Orthogonal semantic space,Review credibility,Semantic relevance,Semantic diversity,Semantic coverage
论文评审过程：Received 11 September 2021, Revised 18 April 2022, Accepted 30 April 2022, Available online 6 May 2022, Version of Record 11 May 2022.
论文官网地址：https://doi.org/10.1016/j.elerap.2022.101154
