Generalized Mean Estimation in Monte-Carlo Tree Search
Abstract
Explanations help users make sense of recommendations, increasing the likelihood of adoption. Existing approaches to explainable recommendations tend to rely on rigidly standardized templates, only allowing fill-in-the-blank aspect-level sentiments. For more flexible, literate, and varied explanations that cover various aspects of interest, we propose to synthesize an explanation by selecting snippets from reviews to optimize representativeness and coherence. To fit the target user's aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of varying product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text generation.
Cite
Text
Dam et al. "Generalized Mean Estimation in Monte-Carlo Tree Search." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/332Markdown
[Dam et al. "Generalized Mean Estimation in Monte-Carlo Tree Search." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/dam2020ijcai-generalized/) doi:10.24963/IJCAI.2020/332BibTeX
@inproceedings{dam2020ijcai-generalized,
title = {{Generalized Mean Estimation in Monte-Carlo Tree Search}},
author = {Dam, Tuan and Klink, Pascal and D'Eramo, Carlo and Peters, Jan and Pajarinen, Joni},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {2397-2404},
doi = {10.24963/IJCAI.2020/332},
url = {https://mlanthology.org/ijcai/2020/dam2020ijcai-generalized/}
}