394 citations
- University of California, BerkeleyUS5 papers
- Google (United States)US4 papers
- Microsoft (United States)US4 papers
- University of Illinois Urbana-ChampaignUS4 papers
- University of Maryland, College ParkUS4 papers
- Amazon (United States)US3 papers
- Carnegie Mellon UniversityUS3 papers
- Lawrence Berkeley National LaboratoryUS3 papers
- Pennsylvania State UniversityUS3 papers
- Stanford UniversityUS3 papers
- University of California, Santa BarbaraUS3 papers
- California Institute of TechnologyUS2 papers
99 papers
Embedding Subspace Partitioning for Dynamic Multi-Objective Retrieval
Shaobo Zhang, Alice Leung, Yunxiang Ren +13
Modern industrial recommender systems must optimize across competing objectives, balancing semantic relevance with business metrics such as engagement and revenue. While bi-encoder…
Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation
Zhen Zhong, Shini Yang, Liesheng Wei
This study proposes a privacy-enhanced federated learning framework to address secure collaborative training in distributed data environments. The framework integrates Dynamic Diff…
TransX: Scaling Transformer-based Recommendation via Behavioral and Serving Stream Crossings
Da Xu, Liyan Fang, Divya Venugopalan +5
Modern industrial recommender systems (RecSys) increasingly adopt Transformer-based sequence models, with an emerging paradigm that frames recommendation as next-token prediction o…
Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest
Junpeng Hou, XianXing Zhang, Sai Xiao +6
Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content. To support this journey, w…
Personalizing Marketplace Policies with Competing Objectives and Constrained Experiments: Evidence from a Job Marketplace
Yufei Wu, Zhen Yan
Two-sided marketplaces connect distinct user groups whose interests often conflict -- improving outcomes on one side could degrade the other side's experience. To address this chal…
Scalable Network-Aware Experiment Design for Two-Sided Marketplaces
Yi Su, Zhen Yan
Measuring causal effects in networked two-sided marketplaces is challenging due to treatment interference between market participants on different sides. When treatment is applied…