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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2020/336

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