This guide details the end‑to‑end process for developers to integrate the Ctrip WenDao (WorkBuddy) skill, covering environment prerequisites, API token acquisition, configuration methods, command‑line usage, response parsing, common scenarios, security considerations, and troubleshooting steps.
The author recounts a Phuket trip to see sea turtles and demonstrates how Bayesian updating, using prior guide data and new evidence such as season and weather, can estimate the probability of spotting turtles, illustrating the method with a concrete example.
This article presents a comprehensive overview of Feizhu's information‑flow recommendation system, detailing its mixed‑material architecture, cold‑start recall and coarse‑ranking techniques, multi‑modal pre‑training and fine‑tuning, fine‑ranking with user‑state gating, and tiered traffic‑flow mechanisms for content delivery.
This article presents Alibaba Fliggy's personalized marketing platform for travel, detailing its multi‑scene architecture, user‑session modeling, graph‑based recommendation algorithms, cold‑start strategies, cross‑domain user mapping, and a hierarchical travel‑play tag system that together enable large‑scale, real‑time, thousand‑person‑one‑face marketing.
This article presents an in‑depth overview of Alibaba Feizhu's travel‑scene search system, covering its background, architecture, query understanding, tagging, POI mining, synonym extraction, recall strategies, model designs, performance results, and future directions for personalization and explainability.
This article explains how Fliggy's travel recommendation platform tackles recall challenges such as cold‑start users, sparse behavior, itinerary‑specific needs, and periodic repurchase by applying user‑attribute models, graph embeddings, dual‑tower architectures, session‑based methods, and statistical repurchase forecasting to improve candidate selection and overall recommendation performance.
This article presents Alibaba Fliggy's personalized marketing platform for travel, detailing its architecture, scenario and functional abstractions, user‑modeling pipelines, full‑stack traffic control, cold‑start techniques, cross‑domain mapping, heterogeneous graph modeling, and a hierarchical travel‑play tag system to achieve thousand‑person‑one‑face recommendation across daily and promotional scenes.
This article details Ctrip's end‑to‑end personalized recommendation system for travel, covering data collection, candidate recall methods, ranking models, feature engineering practices, and future directions, illustrating how millions of users receive tailored travel suggestions.
Meituan‑Dianping’s travel search team tackles cross‑region queries and noisy data by iteratively refining a four‑step, case‑driven pipeline that classifies intent, segments queries, ranks results with distance and term‑importance models, and employs multi‑stage, parallel recall to steadily boost purchase rate, CTR, and user satisfaction.
The article details Ctrip CTO Gan Quan’s insights on how the travel platform leverages a comprehensive big‑data infrastructure, AI‑driven algorithms, and real‑time user behavior tracking to deliver personalized travel recommendations, improve conversion rates, and shorten user decision cycles across multiple business lines.
This article describes Qunar's personalized demand prediction system for the "Guess You Like" card, detailing how user‑demand associations are mined via rule engines, collaborative filtering, LBS and manual rules, and how ranking models evolve from subjective Bayes to RankBoost and LambdaMart, with experimental evaluation and future LSTM plans.
