Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
1-2020
Abstract
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 explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users' aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text generation.
Keywords
Contextualize, Product categories, Text generations, Via fill
Discipline
Databases and Information Systems | Data Science
Research Areas
Data Science and Engineering
Publication
Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, Yokohama, Japan, January 7-11
First Page
2427
Last Page
2434
ISBN
9780999241165
Identifier
10.24963/ijcai.2020/336
Publisher
IJCAI
City or Country
Los Altos, CA
Embargo Period
5-20-2021
Citation
LE, Trung-Hoang and LAUW, Hady W..
Synthesizing aspect-driven recommendation explanations from reviews. (2020). Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, Yokohama, Japan, January 7-11. 2427-2434.
Available at: https://ink.library.smu.edu.sg/sis_research/5954
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.24963/ijcai.2020/336