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DataFunSummit

Dec 23, 2021 · Artificial Intelligence

User Clustering Techniques in Tencent KanDian: From Traditional Algorithms to N‑gram and action2vec

This article explains how Tencent KanDian analyzes user behavior by introducing the product, describing common clustering scenarios, reviewing traditional unsupervised methods, and detailing advanced path‑based approaches such as N‑gram and action2vec, while discussing their advantages, limitations, and practical applications.

AIBehavior AnalysisN-gram

0 likes · 12 min read

User Clustering Techniques in Tencent KanDian: From Traditional Algorithms to N‑gram and action2vec

DataFunTalk

Nov 8, 2021 · Artificial Intelligence

User Behavior Clustering in Tencent Kankan: From Traditional Unsupervised Methods to N‑gram and action2vec

This article introduces Tencent Kankan's product landscape and explores various user clustering techniques—including classic unsupervised algorithms, N‑gram based sequence clustering, and deep‑learning driven action2vec—detailing their implementation steps, advantages, limitations, and practical insights for product optimization.

Behavior AnalysisN-gramTencent

0 likes · 12 min read

User Behavior Clustering in Tencent Kankan: From Traditional Unsupervised Methods to N‑gram and action2vec

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.

N-gramfastTexthotel

0 likes · 9 min read

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

360 Quality & Efficiency

Dec 21, 2018 · Artificial Intelligence

Machine Learning-Based Test Case Step Recommendation: Data Preprocessing, N‑gram, CBOW, and RNN/LSTM Model Construction

This article explains how to use machine‑learning techniques—including data preprocessing, N‑gram, CBOW, and various RNN/LSTM models—to automatically recommend the next function in a test‑case step sequence, improving writing speed and efficiency for developers.

CBOWLSTMN-gram

0 likes · 4 min read

Machine Learning-Based Test Case Step Recommendation: Data Preprocessing, N‑gram, CBOW, and RNN/LSTM Model Construction