5 papers
Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation
Junru Zhang, Jinyong Wen, Bin Dou +5
Existing large language model (LLM)-based user encoders serialize heterogeneous behavioral logs and compress them into static representations through autoregressive (AR) prediction…
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…
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…
Transferable and Forecastable User Targeting Foundation Model
Bin Dou, Baokun Wang, Yun Zhu +11
User targeting, the process of selecting targeted users from a pool of candidates for non-expert marketers, has garnered substantial attention with the advancements in digital mark…
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation
Bin Dou, Tianyu Zhang, Zhaohui Wang +2
Zero-shot neural scene segmentation, which reconstructs 3D neural segmentation field without manual annotations, serves as an effective way for scene understanding. However, existi…