6 papers
Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning
Dingsu Wang, Filip Ryzner, Kelly He +17
As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on lo…
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest
Olafur Gudmundsson, Bo Zhao, Huayi Liao +15
In this paper, we propose a new solution for addressing the content cold-start problem in industry-scale search and recommender systems. Compared to prior approaches, we have made…
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…
MESH: Scaling Up Retrieval with Heterogeneous Content Unification
Jiaxing Qu, Yilin Chen, Junpeng Hou +4
Optimizing large-scale retrieval hinges on the ability to efficiently surface candidates across diverse content tiers. However, to capture segments such as fresh and long-tail cont…
Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest
Saeed Ebrahimi, Weijie Jiang, Jaewon Yang +3
Pinterest is a leading visual discovery platform where recommender systems (RecSys) are key to delivering relevant, engaging, and fresh content to our users. In this paper, we stud…
Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems
Weijie Jiang, Armando Ordorica, Jaewon Yang +3
User retention is a critical objective for online platforms like Pinterest, as it strengthens user loyalty and drives growth through repeated engagement. A key indicator of retenti…