Mining product textual data for recommendation explanations

• Main Author: LE TRUNG HOANG
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: 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
• Online Access: 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
• Institution: Singapore Management University

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, description, review, question and answers, etc., can be a rich source of information to explain the recommendation. As the explanation task could be decoupled from that of recommendation objective, we can categorize recommendation explanation into integrated approach, that uses a single interpretable model to produce both recommendation and explanation, or pipeline approach, that uses a post-hoc explanation model to produce explanation for recommendation from a black-box or an explainable recommendation model. In addition, we can also view the recommendation explanation as evaluative, assessing the quality of a single product, or comparative, comparing the quality of a product to another product or to multiple products. In this dissertation, we present research works on both integrated and pipeline approaches for recommendation explanations as well as both evaluative and comparative recommendation explanations.