6 papers
Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation
Zixuan Wang, Yuhong Chen, Yuxuan Zhu +10
Industrial recommenders increasingly adopt the pretrain-then-transfer paradigm, yet behavioral distribution drift raises two questions: what to learn from behavior sequences, and h…
Compress, Cross and Scale: Multi-Level Compression Cross Networks for Efficient Scaling in Recommender Systems
Heng Yu, Xiangjun Zhou, Jie Xia +4
Modeling high-order feature interactions efficiently is a central challenge in click-through rate and conversion rate prediction. Modern industrial recommender systems are predomin…
Cold-Starting Podcast Ads and Promotions with Multi-Task Learning on Spotify
Shivam Verma, Hannes Karlbom, Yu Zhao +4
We present a unified multi-objective model for targeting both advertisements and promotions within the Spotify podcast ecosystem. Our approach addresses key challenges in personali…
When Embedding Models Meet: Procrustes Bounds and Applications
Lucas Maystre, Alvaro Ortega Gonzalez, Charles Park +4
Embedding models trained separately on similar data often produce representations that encode stable information but are not directly interchangeable. This lack of interoperability…
Mobile Gamer Lifetime Value Prediction via Objective Decomposition and Reconstruction
Tianwei Li, Yu Zhao, Yunze Li +1
For Internet platforms operating real-time bidding (RTB) advertising service, a comprehensive understanding of user lifetime value (LTV) plays a pivotal role in optimizing advertis…
OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Sunhao Dai, Jiakai Tang, Jiahua Wu +13
Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain…