Inspired by the recent success of Byte Latent Transformer, DP-Rec is proposed, a dynamic latent patching architecture for recommendation that scales effectively to long sequences and achieves a superior efficiency–accuracy trade-off over both non-compressed and fixed-size compression baselines.
Dwipam Katariya, T. Caputo, Akshat Shreemali et al.· Proceedings of the 20th ACM...· 0 citations
Sequential RecSys are central to modern personalization, exploiting user's historical interaction sequences to drive next-step decisions. Deep learning models, particularly CNN and Transformer-based architectures, have proven highly effective at capturing temporal dependencies in these histories. For transparency and t...
Akash Pandey, Kanisha Shah, Addrish Roy et al.· 0 citations
REPREC is a lightweight framework that conditions a frozen LLM using compact user-level representations and consistently improves recommendation performance across different sequential encoders, LLM backbones, and user activity levels.
