1 citations · 2 across the 6 of their papers we have counts for
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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…
Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation
Chengbing Wang, Yang Zhang, Fengbin Zhu +3
Leveraging Large Language Models (LLMs) to harness user-item interaction histories for item generation has emerged as a promising paradigm in generative recommendation. However, th…
Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
Tianhao Shi, Yang Zhang, Jizhi Zhang +2
As recommender systems are indispensable in various domains such as job searching and e-commerce, providing equitable recommendations to users with different sensitive attributes b…
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Tianhao Shi, Yang Zhang, Zhijian Xu +4
Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising results. However, the challenges of deploying LLM4Rec in real-world scenarios remain largely unexplo…