1 citations · 1 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…
Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation
Chengbing Wang, Wentao Shi, Jizhi Zhang +3
Recent work has improved recommendation models remarkably by equipping them with debiasing methods. Due to the unavailability of fully-exposed datasets, most existing approaches re…