Publication Type

PhD Dissertation

Version

publishedVersion

Publication Date

11-2022

Abstract

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.

Keywords

Recommender Systems, Recommendation Explanations, Aspect-Level Sentiment, Review-Level Explanation, Question-Level Explanation, Evaluative Recommendation Explanation, Comparative Recommendation Explanations, Review Selection, Review Sets Selection

Degree Awarded

PhD in Computer Science

Discipline

Databases and Information Systems

Supervisor(s)

LAUW, Hady Wirawan

First Page

1

Last Page

146

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

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