3 papers
cs.IR2026
Towards a Densing Law for User Representation Learning at Billion-Scale Capacity
Bin Dou, Junru Zhang, Zhaoyi Yuan +6
User representation learning in real-world industrial scenarios is commonly scaled by increasing user amount, behavioral sequence length and model size. However, existing methods f…
cs.CL2026
How Do Decoder-Only LLMs Perceive Users? Rethinking Attention Masking for User Representation Learning
Jiahao Yuan, Yike Xu, Jinyong Wen +8
Decoder-only large language models are increasingly used as behavioral encoders for user representation learning, yet the impact of attention masking on the quality of user embeddi…
cs.LG2025
FOUNDv2: Learning Unified User Quantized Tokenizers for User Representation
Chuan He, Yang Chen, Bin Dou +10
User representation learning serves as a fundamental pillar for personalized services on large-scale web platforms. Despite its importance, conventional continuous embedding method…