428 citations · 582 across the 17 of their papers we have counts for
15 papers · 1 filter
Towards Sample-Efficient and Stable Reinforcement Learning for LLM-based Recommendation
Hongxun Ding, Keqin Bao, Jizhi Zhang +4
While Long Chain-of-Thought (Long CoT) reasoning has shown promise in Large Language Models (LLMs), its adoption for enhancing recommendation quality is growing rapidly. In this wo…
Decoding in Latent Spaces for Efficient Inference in LLM-based Recommendation
Chengbing Wang, Yang Zhang, Zhicheng Wang +4
Fine-tuning large language models (LLMs) for recommendation in a generative manner has delivered promising results, but encounters significant inference overhead due to autoregress…
Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
Shanle Zheng, Keqin Bao, Jizhi Zhang +3
LLM-based recommender systems have made significant progress; however, the deployment cost associated with the large parameter volume of LLMs still hinders their real-world applica…
K-order Ranking Preference Optimization for Large Language Models
Shihao Cai, Chongming Gao, Yang Zhang +5
To adapt large language models (LLMs) to ranking tasks, existing list-wise methods, represented by list-wise Direct Preference Optimization (DPO), focus on optimizing partial-order…
Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning
Keqin Bao, Ming Yan, Yang Zhang +4
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…
Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation
Yang Zhang, Juntao You, Yimeng Bai +4
Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, curren…
Agentic Feedback Loop Modeling Improves Recommendation and User Simulation
Shihao Cai, Jizhi Zhang, Keqin Bao +4
Large language model-based agents are increasingly applied in the recommendation field due to their extensive knowledge and strong planning capabilities. While prior research has p…
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation
Yang Zhang, Keqin Bao, Ming Yan +3
When adapting Large Language Models for Recommendation (LLMRec), it is crucial to integrate collaborative information. Existing methods achieve this by learning collaborative embed…
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation
Keqin Bao, Jizhi Zhang, Yang Zhang +3
Adapting Large Language Models (LLMs) for recommendation requires careful consideration of the decoding process, given the inherent differences between generating items and natural…
Prospect Personalized Recommendation on Large Language Model-based Agent Platform
Jizhi Zhang, Keqin Bao, Wenjie Wang +5
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…
Item-side Fairness of Large Language Model-based Recommendation System
Meng Jiang, Keqin Bao, Jizhi Zhang +4
Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Lang…
CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
Yang Zhang, Fuli Feng, Jizhi Zhang +3
Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches…
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
Keqin Bao, Jizhi Zhang, Wenjie Wang +6
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 cru…
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
Jizhi Zhang, Keqin Bao, Yang Zhang +3
The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM). Nevertheless, it is imp…
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
Keqin Bao, Jizhi Zhang, Yang Zhang +3
Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation sys…