Search Results for author: Keqin Bao

Found 9 papers, 8 papers with code

Prospect Personalized Recommendation on Large Language Model-based Agent Platform

1 code implementation28 Feb 2024 Jizhi Zhang, Keqin Bao, Wenjie Wang, Yang Zhang, Wentao Shi, Wanhong Xu, Fuli Feng, Tat-Seng Chua

Additionally, we prospect the evolution of Rec4Agentverse and conceptualize it into three stages based on the enhancement of the interaction and information exchange among Agent Items, Agent Recommender, and the user.

Language Modelling Large Language Model +1

Item-side Fairness of Large Language Model-based Recommendation System

1 code implementation23 Feb 2024 Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He

Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS.

Fairness Language Modelling +2

CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation

1 code implementation30 Oct 2023 Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, Xiangnan He

In pursuit of superior recommendations for both cold and warm start scenarios, we introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates collaborative information into LLMs for recommendation.

A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

1 code implementation16 Aug 2023 Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Chong Chen, Fuli Feng, Qi Tian

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 recommendations.

Collaborative Filtering Recommendation Systems

Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation

1 code implementation12 May 2023 Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He

The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM).

Fairness Language Modelling +1

TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

1 code implementation30 Apr 2023 Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He

We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples.

Domain Generalization In-Context Learning +3

Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors

no code implementations17 Feb 2023 Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie

In this paper, we propose Fine-Grained Translation Error Detection (FG-TED) task, aiming at identifying both the position and the type of translation errors on given source-hypothesis sentence pairs.

Position Sentence +1

Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task

1 code implementation18 Oct 2022 Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie

In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation).

Sentence XLM-R

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