Uncertainty-Aware Explainable Recommendation with Large Language Models-Reference-Cited by-同舟云学术

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Uncertainty-Aware Explainable Recommendation with Large Language Models

Published:2024-06-30 Issue: Volume:32 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Peng Yicui¹,Chen Hao¹,Lin Ching-Sheng²,Huang Guo³,Hu Jinrong¹,Guo Hui⁴,Kong Bin⁵,Hu Shu⁶,Wu Xi¹,Wang Xin⁷

Affiliation:

1. Chengdu University of Information Technology,Chengdu,China

2. Tunghai University,Taichung,Taiwan

3. Leshan Normal University,Leshan,China

4. University at Buffalo,SUNY,NY,USA

5. Meta,CA,USA

6. Purdue University,Indianapolis,USA

7. University at Albany,SUNY,NY,USA

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10651104.pdf?arnumber=10651104

Reference45 articles.

1. Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation

2. Matrix Factorization Techniques for Recommender Systems

3. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

4. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

5. Unified language model pre-training for natural language understanding and generation;Dong;Advances in neural information processing systems,2019

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