no code implementations • 25 Mar 2024 • Ziyan Wang, Yingpeng Du, Zhu Sun, Haoyan Chua, Kaidong Feng, Wenya Wang, Jie Zhang
However, the former methods struggle with optimal prompts to elicit the correct reasoning of LLMs due to the lack of task-specific feedback, leading to unsatisfactory recommendations.
no code implementations • 14 Feb 2024 • Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua, Hongzhi Liu, Zhonghai Wu, Yining Ma, Jie Zhang, Youchen Sun
To adapt text-based LLMs with structured graphs, We use the LLM as an aggregator in graph processing, allowing it to understand graph-based information step by step.
no code implementations • 7 Nov 2023 • Wenxuan Zhang, Hongzhi Liu, Yingpeng Du, Chen Zhu, Yang song, HengShu Zhu, Zhonghai Wu
Nevertheless, these methods encounter the certain issue that information such as community behavior pattern in RS domain is challenging to express in natural language, which limits the capability of LLMs to surpass state-of-the-art domain-specific models.
no code implementations • 20 Jul 2023 • Yingpeng Du, Di Luo, Rui Yan, Hongzhi Liu, Yang song, HengShu Zhu, Jie Zhang
However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion.
1 code implementation • 15 Dec 2020 • Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yingpeng Du
Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations.