13 citations · 31 across the 22 of their papers we have counts for
5 papers · 1 filter
RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
Jianxun Lian, Yuxuan Lei, Xu Huang +3
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI…
Aligning Language Models for Versatile Text-based Item Retrieval
Yuxuan Lei, Jianxun Lian, Jing Yao +3
This paper addresses the gap between general-purpose text embeddings and the specific demands of item retrieval tasks. We demonstrate the shortcomings of existing models in capturi…
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…
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,…
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…