12 papers
DREAM Technical Report
Bin Zhang, Bowen Zheng, Chao Yi +74
Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across mo…
ClawRec: A Claw-Native Recommender System
Chenghao Wu, Kesha Ou, Xiaolei Wang +8
Recommender systems have become integral to navigating the modern digital ecosystem. Yet most deployed systems remain confined within single-platform boundaries, observing localize…
RecGPT-V3 Technical Report
Bowen Zheng, Chao Yi, Dian Chen +26
Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecG…
Guess Where You Go: Generative Next Point-of-Interest Recommendation in Amap
Penglong Zhai, Bowen Zheng, Jie Li +8
Generative retrieval enables recommender systems to retrieve items by generating compact item identifiers, but scaling it to industrial scenarios remains challenging due to redunda…
Generative Spatiotemporal Intent Sequence Recommendation via Implicit Reasoning in Amap
Sicong Wang, Ruiting Dong, Yue Liu +7
Real-world user behavior rarely consists of isolated actions; instead, it often forms intent flows governed by spatiotemporal dependencies. To provide integrated service recommenda…
LARES: Latent Reasoning for Sequential Recommendation
Enze Liu, Bowen Zheng, Xiaolei Wang +4
Sequential recommender systems have become increasingly important in real-world applications that model user behavior sequences to predict their preferences. However, existing sequ…