6 papers · 1 filter
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…
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…
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…
Learning to Alleviate Familiarity Bias in Video Recommendation
Zheng Ren, Yi Wu, Jianan Lu +4
Modern video recommendation systems aim to optimize user engagement and platform objectives, yet often face structural exposure imbalances caused by behavioral biases. In this work…
Selecting User Histories to Generate LLM Users for Cold-Start Item Recommendation
Nachiket Subbaraman, Jaskinder Sarai, Aniruddh Nath +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning, generalization, and simulating human-like behavior across a wide range of tasks. These strength…
ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems
Daryl Chang, Yi Wu, Jennifer She +2
Recommender systems often must maximize a primary objective while ensuring secondary ones satisfy minimum thresholds, or "guardrails." This is critical for maintaining a consistent…