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Aug 31, 2023 · Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations. Xu Huang, Jianxun Lian, Yuxuan Lei, Jing Yao, Defu Lian, Xing Xie. Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data.
- arXiv:2308.16505 [cs.IR]
- 18 pages, 17 figures, 7 tables
Jan 30, 2024 · Xu Huang1, Jianxun Lian21, Yuxuan Lei1, Jing Yao2, Defu Lian11, Xing Xie2. Abstract. Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data.
Defu Lian (连德富) Professor, University of Science and Technology of China. Verified email at ustc.edu.cn - Homepage. Recommender Systems Data Mining Deep Learning. Title. Sort. Sort by citations Sort by year Sort by title. Cited by.
However, as users increasingly rely on conversational in-terfaces for discovering and exploring products, there is a growing need to develop more sophisticated and interactive recommendation systems that can understand and respond effectively to diverse user inquiries and intents in an conver-sational manner.
We apply stacked denoising auto-encoders and stacked convolutional auto-encoders, which are two types of deep learning based embedding techniques, to extract items' textual representations and visual representations, respectively.
Aug 31, 2023 · Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations. 31 Aug 2023 · Xu Huang , Jianxun Lian , Yuxuan Lei , Jing Yao , Defu Lian , Xing Xie ·. Edit social preview. Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data.
1. INTRODUCTION. Due to the explosive growth of information, recommender sys-tems have been playing an increasingly important role in online ser-vices. Among different recommendation strategies, collaborative.
