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20232026
most citedRecExplainer: Aligning Large Language Models for Explaining Recommendation Models

24 citations · 66 across the 24 of their papers we have counts for

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5 papers · 1 filter

cs.IR2023★ 7 cited

Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations

Jing Yao, Wei Xu, Jianxun Lian +3

The significant progress of large language models (LLMs) provides a promising opportunity to build human-like systems for various practical applications. However, when applied to s…

cs.CL2023★ 3 cited

Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values

Jing Yao, Xiaoyuan Yi, Xiting Wang +2

The rapid advancement of Large Language Models (LLMs) has attracted much attention to value alignment for their responsible development. However, how to define values in this conte…

cs.IR2023★ 24 cited

RecExplainer: Aligning Large Language Models for Explaining Recommendation Models

Yuxuan Lei, Jianxun Lian, Jing Yao +3

Recommender systems are widely used in online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However,…

cs.CY2023★ 5 cited

Unpacking the Ethical Value Alignment in Big Models

Xiaoyuan Yi, Jing Yao, Xiting Wang +1

Big models have greatly advanced AI's ability to understand, generate, and manipulate information and content, enabling numerous applications. However, as these models become incre…

cs.IR2023★ 11 cited

Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations

Xu Huang, Jianxun Lian, Yuxuan Lei +3

Recommender models excel at providing domain-specific item recommendations by leveraging extensive user behavior data. Despite their ability to act as lightweight domain experts, t…