Search Results for author: Zang Li

Found 9 papers, 4 papers with code

Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm

1 code implementation16 Feb 2024 Yuanzhen Xie, Xinzhou Jin, Tao Xie, Mingxiong Lin, Liang Chen, Chenyun Yu, Lei Cheng, Chengxiang Zhuo, Bo Hu, Zang Li

To improve the contextual learning capabilities of LLMs in text-to-SQL, a workflow paradigm method is proposed, aiming to enhance the attention and problem-solving scope of LLMs through decomposition.

Active Learning In-Context Learning +1

OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities

no code implementations23 May 2023 Yuanzhen Xie, Tao Xie, Mingxiong Lin, WenTao Wei, Chenglin Li, Beibei Kong, Lei Chen, Chengxiang Zhuo, Bo Hu, Zang Li

At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks.

Active Learning Decision Making +1

One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation

1 code implementation22 Nov 2022 Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, XiaoHu Qie, Di Niu

CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain.

Multi-Domain Recommender Systems Recommendation Systems +1

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

2 code implementations13 Oct 2022 Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun Yu, Bo Hu, Zang Li, Yu Xu, XiaoHu Qie

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.

Recommendation Systems

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

no code implementations13 Jun 2022 Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M. Jose, Chenyun Yu, Beibei Kong, Xiangnan He, Zhijin Wang, Bo Hu, Zang Li

That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.

Recommendation Systems Transfer Learning

An Attention-based Graph Neural Network for Heterogeneous Structural Learning

1 code implementation19 Dec 2019 Huiting Hong, Hantao Guo, Yu-Cheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.

Graph Embedding Graph Representation Learning +2

AHINE: Adaptive Heterogeneous Information Network Embedding

no code implementations20 Aug 2019 Yu-Cheng Lin, Xiaoqing Yang, Zang Li, Jieping Ye

In this paper, we propose two novel algorithms, GHINE (General Heterogeneous Information Network Embedding) and AHINE (Adaptive Heterogeneous Information Network Embedding), to compute distributed representations for elements in heterogeneous networks.

Link Prediction Network Embedding +1

Cannot find the paper you are looking for? You can Submit a new open access paper.