Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Bao, Keqin"'
Frequently updating Large Language Model (LLM)-based recommender systems to adapt to new user interests -- as done for traditional ones -- is impractical due to high training costs, even with acceleration methods. This work explores adapting to dynam
Externí odkaz:
http://arxiv.org/abs/2410.23136
Autor:
Zhang, Yang, You, Juntao, Bai, Yimeng, Zhang, Jizhi, Bao, Keqin, Wang, Wenjie, Chua, Tat-Seng
Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, current LLM-based approaches struggle to fully leverage user behavior sequen
Externí odkaz:
http://arxiv.org/abs/2410.22809
Agents powered by large language models have shown remarkable reasoning and execution capabilities, attracting researchers to explore their potential in the recommendation domain. Previous studies have primarily focused on enhancing the capabilities
Externí odkaz:
http://arxiv.org/abs/2410.20027
Adapting Large Language Models (LLMs) for recommendation requires careful consideration of the decoding process, given the inherent differences between generating items and natural language. Existing approaches often directly apply LLMs' original dec
Externí odkaz:
http://arxiv.org/abs/2406.14900
Large language models have seen widespread adoption in math problem-solving. However, in geometry problems that usually require visual aids for better understanding, even the most advanced multi-modal models currently still face challenges in effecti
Externí odkaz:
http://arxiv.org/abs/2406.11503
When adapting Large Language Models for Recommendation (LLMRec), it is crucial to integrate collaborative information. Existing methods achieve this by learning collaborative embeddings in LLMs' latent space from scratch or by mapping from external m
Externí odkaz:
http://arxiv.org/abs/2406.03210
Autor:
Zhang, Jizhi, Bao, Keqin, Wang, Wenjie, Zhang, Yang, Shi, Wentao, Xu, Wanhong, Feng, Fuli, Chua, Tat-Seng
The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Ag
Externí odkaz:
http://arxiv.org/abs/2402.18240
Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Language Models-based Recommendation System (LRS) may introduce additional
Externí odkaz:
http://arxiv.org/abs/2402.15215
Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches prioritize text semantics, usually neglecting the valuable collaborat
Externí odkaz:
http://arxiv.org/abs/2310.19488
Autor:
Bao, Keqin, Zhang, Jizhi, Wang, Wenjie, Zhang, Yang, Yang, Zhengyi, Luo, Yancheng, Chen, Chong, Feng, Fuli, Tian, Qi
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a crucial role in augmenting their effectiveness in providing recommendatio
Externí odkaz:
http://arxiv.org/abs/2308.08434