Mining product textual data for recommendation explanations

• 主要作者: LE TRUNG HOANG
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2022
• 主題: Recommender Systems; Recommendation Explanations; Aspect-Level Sentiment; Review-Level Explanation; Question-Level Explanation; Evaluative Recommendation Explanation; Comparative Recommendation Explanations; Review Selection; Review Sets Selection; Databases and Information Systems
• 在線閱讀: https://ink.library.smu.edu.sg/etd_coll/450; https://ink.library.smu.edu.sg/context/etd_coll/article/1448/viewcontent/GPIS_AY2017_PhD_LE_Trung_Hoang.pdf

Recommendation explanations help to make sense of recommendations, increasing the likelihood of adoption. Here, we are interested in mining product textual data, an unstructured data type, coming from manufacturers, sellers, or consumers, appearing in many places including title, summary, descriptio...