4 papers · 1 filter
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…
TabEmbed: Benchmarking and Learning Generalist Embeddings for Tabular Understanding
Minjie Qiang, Mingming Zhang, Xiaoyi Bao +5
Foundation models have established unified representations for natural language processing, yet this paradigm remains largely unexplored for tabular data. Existing methods face fun…
Query as Anchor: Scenario-Adaptive User Representation via Large Language Model
Jiahao Yuan, Yike Xu, Jinyong Wen +9
Industrial-scale user representation learning requires balancing robust universality with acute task-sensitivity. However, existing paradigms primarily yield static, task-agnostic…
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…