Self-Explainable Next POI Recommendation-Reference-Cited by-同舟云学术

Self-Explainable Next POI Recommendation

Published:2024-07-10 Issue: Volume:4 Page:2619-2623
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Yang Kai¹^ORCID,Yang Yi¹^ORCID,Gao Qiang²^ORCID,Zhong Ting¹^ORCID,Wang Yong³^ORCID,Zhou Fan⁴^ORCID

Affiliation:

1. University of Electronic Science and Technology of China, Chengdu, China

2. Southwestern University of Finance and Economics, Chengdu, China

3. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

4. University of Electronic Science and Technology of China & Intelligent Terminal Key Laboratory of Sichuan Province, Chengdu, China

Funder

Intelligent Terminal Key Laboratory of Sichuan Province

National Natural Science Foundation of China

Sichuan Science and Technology Program

Natural Science Foundation of Sichuan Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657967

Reference32 articles.

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2. Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach

3. Changdong Chen, Allen Ding Tian, and Ruochen Jiang. 2023. When Post Hoc Explanation Knocks: Consumer Responses to Explainable AI Recommendations. Journal of Interactive Marketing (2023), 10949968231200221.

4. Chen Cheng Haiqin Yang Michael R Lyu and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Twenty-Third international joint conference on Artificial Intelligence.

5. Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new poi recommendation. (2015).