Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2025
Abstract
Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered. This work extends the notion of comparative explanations, by going beyond merely better/worse statements, to further incorporate aspect-level opinions for more informative comparisons. To enhance the quality of both the personalized recommendation and the explanation, we incorporate optimization objectives that preserve relative rankings of aspects and opinions, in addition to the classical rankings of overall preferences for items. We integrate the multiple ranking objectives and multi-tensor factorization together. Experiments on datasets of different domains validate the efficacy of our proposed framework in both recommendation and comparative explanation against comparable explainable recommendation baselines.
Keywords
Recommender systems, Multi-tensor factorization, Comparative explanation, Aspect-level opinion
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Machine Learning
Volume
114
Issue
17
First Page
1
Last Page
20
ISSN
0885-6125
Identifier
10.1007/s10994-024-06699-5
Publisher
Springer
Citation
LE, Trung Hoang and LAUW, Hady Wirawan.
Learning to rank aspects and opinions for comparative explanations. (2025). Machine Learning. 114, (17), 1-20.
Available at: https://ink.library.smu.edu.sg/sis_research/10149
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/10.1007/s10994-024-06699-5