22 citations · 24 across the 5 of their papers we have counts for
5 papers
NOVA: A Verification-Aware Agent Harness for Architecture Evolution in Industrial Recommender Systems
Shaohua Liu, Liang Fang, Yilong Sun +16
Industrial advertising recommender systems are continually improved through architecture modifications, yet production iteration remains expert-intensive because coordinated change…
RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems
Jin Chen, Shangyu Zhang, Bin Hu +16
The scaling laws for recommender systems have been increasingly validated, where MetaFormer-based architectures consistently benefit from increased model depth, hidden dimensionali…
Large Foundation Model for Ads Recommendation
Shangyu Zhang, Shijie Quan, Zhongren Wang +30
Online advertising relies on accurate recommendation models, with recent advances using pre-trained large-scale foundation models (LFMs) to capture users' general interests across…
Ads Recommendation in a Collapsed and Entangled World
Junwei Pan, Wei Xue, Ximei Wang +7
We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations. Our study begins by showcasing our a…
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Liangcai Su, Junwei Pan, Ximei Wang +4
Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negativ…