134 citations · 445 across the 29 of their papers we have counts for
14 papers · 1 filter
Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models
Xilun Chen, Shao-Chuan Wang, Baykal Cakici +6
Large Recommendation Models (LRMs) have demonstrated promising capabilities in industry-scale recommendation tasks. However, holistically integrating traditional signals into these…
Tokens are All You Need: Dual-purpose Semantic IDs for Achieving LLM-Level I/O Efficiency in recommendation systems
Baolei Li, Yiping Yuan, Yilin Zheng +6
Large-scale recommendation systems face "Memory Wall" bottlenecks due to massive, dense embedding tables. While generative retrieval uses discrete tokens for IDs, high-dimensional…
TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
Qingyun Liu, Bo Yan, Yang Liu +15
User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors. An emergi…
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…
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…
STAR: A Simple Training-free Approach for Recommendations using Large Language Models
Dong-Ho Lee, Adam Kraft, Long Jin +5
Recent progress in large language models (LLMs) offers promising new approaches for recommendation system tasks. While the current state-of-the-art methods rely on fine-tuning LLMs…
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…
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…
Density Weighting for Multi-Interest Personalized Recommendation
Nikhil Mehta, Anima Singh, Xinyang Yi +3
Using multiple user representations (MUR) to model user behavior instead of a single user representation (SUR) has been shown to improve personalization in recommendation systems.…
Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations
Anima Singh, Trung Vu, Nikhil Mehta +9
Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, caus…
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
Yin Zhang, Ruoxi Wang, Tiansheng Yao +5
Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts…
Reward Shaping for User Satisfaction in a REINFORCE Recommender
Konstantina Christakopoulou, Can Xu, Sai Zhang +10
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction? Three research questions are key…
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao +3
Highly skewed long-tail item distribution is very common in recommendation systems. It significantly hurts model performance on tail items. To improve tail-item recommendation, we…
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen +4
Recommender system models often represent various sparse features like users, items, and categorical features via embeddings. A standard approach is to map each unique feature valu…