Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article explains how Fliggy combines interactive recommendation with travel‑theme recommendation, detailing the underlying algorithms, user‑demand classification, real‑time interest capture, recall strategies, multi‑task learning for CTR prediction, and engineering tricks that improve personalization and click‑through rates.

AlibabaFliggyRecommendation Systems

0 likes · 17 min read

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

Alibaba Cloud Developer

Jan 9, 2020 · Artificial Intelligence

How Edge Computing Transforms Real-Time Recommendation Systems

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.

behavior modelingon-device AIreal-time personalization

0 likes · 16 min read

How Edge Computing Transforms Real-Time Recommendation Systems

Alibaba Cloud Developer

Dec 26, 2019 · Artificial Intelligence

How Edge Intelligence Revolutionizes Real-Time Recommendations on Mobile Apps

Alibaba’s Taobao team applied large‑scale edge computing and deep neural networks on the client side to continuously sense user intent, enabling real‑time, personalized recommendations that significantly boosted click‑through rates and GMV during major shopping events.

Edge ComputingMobilemachine learning

0 likes · 15 min read

How Edge Intelligence Revolutionizes Real-Time Recommendations on Mobile Apps