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    <abstract>The bloom of the Internet and the recent breakthroughs in deep learning techniques open a new door to AI for E-commence, with a trend of evolving from using a few financial factors such as liquidity and profitability to using more advanced AI techniques to process complex and multi-modal data. In this paper, we tackle the practical problem of restaurant survival prediction. We argue that traditional methods ignore two essential respects, which are very helpful for the task: 1) modeling customer reviews and 2) jointly considering status prediction and result explanation. Thus, we propose a novel joint learning framework for explainable restaurant survival prediction based on the multi-modal data of user-restaurant interactions and users’ textual reviews. Moreover, we design a graph neural network to capture the high-order interactions and design a co-attention mechanism to capture the most informative and meaningful signal from noisy textual reviews. Our results on two datasets show a significant and consistent improvement over the SOTA techniques (average 6.8% improvement in prediction and 45.3% improvement in explanation).</abstract>
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%0 Conference Proceedings
%T A Joint Learning Framework for Restaurant Survival Prediction and Explanation
%A Li, Xin
%A Zhang, Xiaojie
%A JiaHao, Peng
%A Mao, Rui
%A Zhou, Mingyang
%A Xie, Xing
%A Liao, Hao
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-joint
%X The bloom of the Internet and the recent breakthroughs in deep learning techniques open a new door to AI for E-commence, with a trend of evolving from using a few financial factors such as liquidity and profitability to using more advanced AI techniques to process complex and multi-modal data. In this paper, we tackle the practical problem of restaurant survival prediction. We argue that traditional methods ignore two essential respects, which are very helpful for the task: 1) modeling customer reviews and 2) jointly considering status prediction and result explanation. Thus, we propose a novel joint learning framework for explainable restaurant survival prediction based on the multi-modal data of user-restaurant interactions and users’ textual reviews. Moreover, we design a graph neural network to capture the high-order interactions and design a co-attention mechanism to capture the most informative and meaningful signal from noisy textual reviews. Our results on two datasets show a significant and consistent improvement over the SOTA techniques (average 6.8% improvement in prediction and 45.3% improvement in explanation).
%R 10.18653/v1/2022.emnlp-main.216
%U https://aclanthology.org/2022.emnlp-main.216
%U https://doi.org/10.18653/v1/2022.emnlp-main.216
%P 3285-3297

Markdown (Informal)

[A Joint Learning Framework for Restaurant Survival Prediction and Explanation](https://aclanthology.org/2022.emnlp-main.216) (Li et al., EMNLP 2022)

• A Joint Learning Framework for Restaurant Survival Prediction and Explanation (Li et al., EMNLP 2022)

ACL

• Xin Li, Xiaojie Zhang, Peng JiaHao, Rui Mao, Mingyang Zhou, Xing Xie, and Hao Liao. 2022. A Joint Learning Framework for Restaurant Survival Prediction and Explanation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3285–3297, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.