Long-sequence modeling is increasingly important in recommender systems for capturing users’ evolving and long-term interests. In advertising, however, user interaction histories are often highly sparse due to limited exposure opportunities, making ad-only behavior sequences insufficient for effective long-sequence rec...
Xian Hu, Ming Yue, Zhi-Xiang Feng et al.· Proceedings of the 20th ACM...· 0 citations
ChronicleRec is a pre-train-and-transfer framework that compresses an ultra-long behavior sequence once into a chronologically ordered set of Chronicle Tokens, which can be cached per user, decoupling ultra-long sequence modeling from online candidate scoring.
Chengkai Huang, Yu-Bin Sheng, Liang Guo et al.· 0 citations
UniTraj, a practical framework that extends sequence construction beyond the advertising domain by incorporating behaviors from content-consumption scenarios, forming unified commercial trajectories across domains and scenarios, is proposed and deployed in a large-scale online advertising system.
Xian Hu, Ming Yue, Zhi-Xiang Feng et al.· Proceedings of the 20th ACM...· 0 citations
KuaFu is a unified behavior-compression layer whose minimal unit is one behavior item, with fidelity-oriented four-stage training and layered intermediate evaluation, which matches or exceeds uncompressed single-task production models on all five headline metrics, raises per-GPU throughput by 37%-350%, and saves 190 GP...
Jia-Hao Hui, Lin Zhu, Yi-Sheng Hu et al.· 0 citations
DLMRec is proposed, a discrete diffusion language model tailored for recommendation that offers a compelling alternative to autoregressive generation and introduces three key components to bridge diffusion language modeling with recommendation.
Chengyi Liu, Yong-Qi Zhou, Jun-Wei Pan et al.· arXiv.org· 1 citation
DASH is a decision-aware user simulator that jointly generates thinking traces and predicts behavioral actions from heterogeneous cross-domain histories and tailors a rubric-based reward model that evaluates thinking traces along form, content, and logic for RL training.
