708 citations · 972 across the 25 of their papers we have counts for
12 papers · 1 filter
Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems
Nikhil Khani, Shuo Yang, Aniruddh Nath +9
Knowledge Distillation (KD) is a powerful approach for compressing a large model into a smaller, more efficient model, particularly beneficial for latency-sensitive applications li…
Leveraging LLM Reasoning Enhances Personalized Recommender Systems
Alicia Y. Tsai, Adam Kraft, Long Jin +7
Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. While t…
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…
Aligning Large Language Models with Recommendation Knowledge
Yuwei Cao, Nikhil Mehta, Xinyang Yi +5
Large language models (LLMs) have recently been used as backbones for recommender systems. However, their performance often lags behind conventional methods in standard tasks like…
Large Language Models as Data Augmenters for Cold-Start Item Recommendation
Jianling Wang, Haokai Lu, James Caverlee +2
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation…
Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
Huan Gui, Ruoxi Wang, Ke Yin +5
Learning feature interaction is the critical backbone to building recommender systems. In web-scale applications, learning feature interaction is extremely challenging due to the s…