3 citations · 4 across the 7 of their papers we have counts for
8 papers
Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation
Shuo Chang, Yueqi Wang, Zihuan Diao +7
Recommender systems rely heavily on heterogeneous behavioral feedback to infer user preference. Although abundant, these signals are imperfect measurements: the same observed behav…
Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators
Zhengyang Su, Isay Katsman, Yueqi Wang +10
Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit from restricting the output space to a c…
Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation Updates
Changping Meng, Hongyi Ling, Jianling Wang +9
Large Language Models (LLMs) empower recommendation systems through their advanced reasoning and planning capabilities. However, the dynamic nature of user interests and content po…
PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
Ruining He, Lukasz Heldt, Lichan Hong +20
Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit signi…
LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
Boyuan Long, Yueqi Wang, Hiloni Mehta +10
This paper presents a case study on deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content…
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems
Jianling Wang, Yifan Liu, Yinghao Sun +11
Exploration, the act of broadening user experiences beyond their established preferences, is challenging in large-scale recommendation systems due to feedback loops and limited sig…
LLMs for User Interest Exploration in Large-scale Recommendation Systems
Jianling Wang, Haokai Lu, Yifan Liu +9
Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel u…
Reducing Symbiosis Bias Through Better A/B Tests of Recommendation Algorithms
Jennifer Brennan, Yahu Cong, Yiwei Yu +6
It is increasingly common in digital environments to use A/B tests to compare the performance of recommendation algorithms. However, such experiments often violate the stable unit…