Pinterest has introduced PinnerFormer, a state-of-the-art sequence modeling approach for learning user representations that power personalized recommendations on their platform. PinnerFormer aims to predict users' long-term engagement with Pins based on their recent actions, enabling Pinterest to surface the most relevant and engaging content to over 400 million monthly users. …
The paper introduces Progressive Layered Extraction (PLE), a novel Multi-Task Learning (MTL) model, aimed at overcoming the challenges in recommender systems, particularly the seesaw phenomenon and negative transfer. Traditional MTL models often struggle with performance degradation due to complex task correlations within real-world recommender systems. …
The paper presents Hiformer, an innovative Transformer-based model tailored for recommender systems, emphasizing efficient heterogeneous feature interaction learning. Traditional Transformer architectures face significant hurdles in recommender systems, notably in capturing the complex interplay of diverse features and achieving acceptable serving latency for web-scale applications. …