travel recommendation

DataFunSummit

Mar 30, 2022 · Artificial Intelligence

Travel Intent Prediction in E-commerce: Algorithm Strategies, Multi-Source Behavior Modeling, and Experimental Results

This article presents a comprehensive overview of travel intent prediction at Alibaba's Fliggy platform, detailing the unique challenges of low‑frequency travel behavior, multi‑source user actions, a multi‑CNN and time‑attention model design, and experimental evaluations that demonstrate improved recommendation performance.

AImulti-CNNmulti-source behavior

0 likes · 10 min read

Travel Intent Prediction in E-commerce: Algorithm Strategies, Multi-Source Behavior Modeling, and Experimental Results

DataFunSummit

Jan 3, 2022 · Artificial Intelligence

Exploration of Alibaba's Feizhu Recommendation Algorithms and Full‑Space CVR Estimation Models (ESMM, ESM², HM³)

This article presents an in‑depth overview of Alibaba's e‑commerce and travel recommendation systems, covering the evolution of full‑space CVR estimation models such as ESMM, ESM² and HM³, their architectural components, challenges, and practical applications in the Feizhu platform.

AlibabaCVR estimationFull‑Space Modeling

0 likes · 25 min read

Exploration of Alibaba's Feizhu Recommendation Algorithms and Full‑Space CVR Estimation Models (ESMM, ESM², HM³)

DataFunTalk

Mar 4, 2021 · Artificial Intelligence

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article presents the design and implementation of interactive recommendation and travel‑theme recommendation in Alibaba's Fliggy app, covering background, user demand classification, real‑time interest capture, various recall strategies, ranking models, multi‑task learning, and engineering tricks to improve CTR and user experience.

AIFliggyMachine Learning

0 likes · 16 min read

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App