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cs.IR2026
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…
cs.IR2026
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…
cs.IR2026
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…