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Ctrip Technology
Ctrip Technology
Apr 9, 2021 · Artificial Intelligence

Algorithm Optimization for Hotel Recommendation and Large‑Scale Discrete DNN Training at Ctrip

This article describes how Ctrip improved hotel recommendation by iterating from logistic regression to GBDT and deep neural networks, designing continuous and discrete features, adopting multi‑task learning with click and conversion signals, and building a large‑scale distributed DNN training and unified feature‑processing framework to boost model accuracy and engineering efficiency.

CtripDNNLarge-Scale Training
0 likes · 15 min read
Algorithm Optimization for Hotel Recommendation and Large‑Scale Discrete DNN Training at Ctrip
Ctrip Technology
Ctrip Technology
Aug 13, 2020 · Artificial Intelligence

Hotel Recommendation System Architecture, Models, and Evaluation at Ctrip

This article presents a comprehensive overview of Ctrip's hotel recommendation system, covering its technical architecture, data processing pipelines, various ranking and embedding models—including FM, Wide&Deep, DeepFM, and FTRL—deployment methods such as PMML and TensorFlow Serving, offline and online evaluation results, and challenges like cold‑start and diversity.

CtripMachine LearningOnline Inference
0 likes · 24 min read
Hotel Recommendation System Architecture, Models, and Evaluation at Ctrip
Qunar Tech Salon
Qunar Tech Salon
Apr 22, 2020 · Backend Development

Optimizing Hotel Pricing Service: Removing the Stateful Architecture of the Sort Interface

This article presents a detailed case study of a hotel pricing system’s stateful Sort interface, analyzes its drawbacks, and describes a comprehensive plan to replace it with a stateless, memory‑based architecture that improves performance, reduces server count, and lowers operational complexity.

BackendPerformancearchitecture
0 likes · 13 min read
Optimizing Hotel Pricing Service: Removing the Stateful Architecture of the Sort Interface
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
Nov 1, 2019 · Artificial Intelligence

Improving International Hotel Room‑Type Merging with Text Similarity and Machine‑Learning Models

This article describes how a large‑scale international hotel platform reduced room‑type merging errors and user complaints by applying rule‑based methods, text‑similarity algorithms (Jaccard, LCS, N‑Gram) and supervised machine‑learning classifiers such as fastText to standardize and merge heterogeneous room‑type data.

Machine LearningN-gramfastText
0 likes · 9 min read
Improving International Hotel Room‑Type Merging with Text Similarity and Machine‑Learning Models
Ctrip Technology
Ctrip Technology
Jun 19, 2019 · Artificial Intelligence

Applying Reinforcement Learning to Hotel Ranking at Ctrip: Challenges, Solutions, and Preliminary Results

This article examines the limitations of traditional learning‑to‑rank for Ctrip hotel sorting, introduces reinforcement learning as a remedy, outlines three progressive implementation plans (A, B, C) with algorithm choices and engineering trade‑offs, and presents early experimental findings that demonstrate RL's potential to improve conversion rates.

CtripRLRanking
0 likes · 15 min read
Applying Reinforcement Learning to Hotel Ranking at Ctrip: Challenges, Solutions, and Preliminary Results
Qunar Tech Salon
Qunar Tech Salon
Jun 5, 2018 · Backend Development

Hotel Quote Search System Architecture and Workflow Overview

This article details the design and operation of a high‑concurrency hotel quote search platform, covering business background, core functionalities, system layers, data fetching, aggregation, scheduling, and price‑update mechanisms to ensure comprehensive, real‑time hotel pricing for users.

BackendSystem Architecturedata aggregation
0 likes · 10 min read
Hotel Quote Search System Architecture and Workflow Overview