Synthesizing aspect-driven recommendation explanations from reviews

• Main Authors: LE, Trung-Hoang, LAUW, Hady W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Contextualize; Product categories; Text generations; Via fill; Databases and Information Systems; Data Science
• Online Access: https://ink.library.smu.edu.sg/sis_research/5954; https://ink.library.smu.edu.sg/context/sis_research/article/6957/viewcontent/0336.pdf
• Institution: Singapore Management University

Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied expla...