This article presents Kuaishou's large‑scale recommendation system, detailing its precision‑ranking architecture, the PPNet CTR model, multi‑domain multi‑task learning, short‑ and long‑term behavior sequence modeling, billion‑feature trillion‑parameter engineering, and future research directions.
This article examines how financial institutions can manage risk and improve operations for existing loan customers by analyzing customer flow during the pandemic, policy impacts, rapid deterioration patterns, and by applying advanced models such as LSTM, survival analysis, and B‑card strategies to enable timely risk detection and targeted cross‑selling.
Meituan’s search ranking system integrates Transformer‑based models across feature engineering, behavior sequence modeling, and re‑ranking, adapting AutoInt‑style embeddings and multi‑stage attention mechanisms to boost QV_CTR and NDCG, while outlining future enhancements with BERT, graph neural networks, and reinforcement learning.
This article examines the limitations of cloud‑based recommendation pipelines, explains how edge computing can provide localized user perception and rapid re‑ranking, describes the EdgeRec on‑device model architecture—including heterogeneous behavior sequence modeling and behavior‑aware attention reranking—and presents offline and online experimental results that demonstrate significant gains in click‑through and conversion rates.
This article introduces the concept of sensory design, explains its psychological basis, outlines a four‑stage workflow—from modeling user behavior to data‑driven validation—and provides practical examples and tools for creating consistent, low‑noise product experiences that align user emotions with design outcomes.
