wide & deep | BestHub

Cognitive Technology Team

Mar 31, 2025 · Artificial Intelligence

Recommendation Algorithms: Using Mathematical Methods for Efficient Information Matching

Recommendation algorithms, rooted in machine learning and deep learning, transform massive user‑generated data into mathematical models that filter and personalize content, covering traditional collaborative filtering, matrix factorization, cosine similarity, and modern deep models such as Wide & Deep and Two‑Tower retrieval, illustrating their evolution and practical applications.

collaborative filteringdeep learningmachine learning

0 likes · 14 min read

Recommendation Algorithms: Using Mathematical Methods for Efficient Information Matching

58 Tech

Aug 10, 2021 · Artificial Intelligence

Active Learning and Model Enhancements for Semantic Tag Mining in 58.com Voice Data

This article presents a comprehensive study on extracting semantic tags from 58.com voice data, detailing the use of active learning to address cold‑start problems, comparing keyword matching, XGBoost, TextCNN, CRNN, and an improved Wide&Deep model, and demonstrating significant reductions in labeling effort and superior F1 scores across multiple experiments.

CRNNText Classificationactive learning

0 likes · 15 min read

Active Learning and Model Enhancements for Semantic Tag Mining in 58.com Voice Data

58 Tech

Dec 25, 2020 · Artificial Intelligence

User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model

This article presents a comprehensive study on C‑side user identity recognition for internet platforms, addressing cold‑start and sample‑scarcity challenges by comparing keyword matching, XGBoost, TextCNN, a fusion model, and an improved Wide & Deep architecture, showing that the latter achieves the highest F1 score of 80.67%.

Cold StartTextCNNXGBoost

0 likes · 13 min read

User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model

Beike Product & Technology

Sep 4, 2020 · Artificial Intelligence

Wide & Deep Model for Real‑Estate Purchase Intent Prediction

This article presents a comprehensive study of the Wide & Deep architecture applied to user purchase‑intent quantification in the real‑estate domain, detailing feature engineering, model design, training procedures, experimental results, and extensions with GRU‑based sequential modeling to improve accuracy.

CTR predictionFeature EngineeringReal Estate

0 likes · 15 min read

Wide & Deep Model for Real‑Estate Purchase Intent Prediction

Tencent Cloud Developer

Sep 3, 2020 · Artificial Intelligence

CTR Prediction Optimization for App Store Recommendation: Integrating DeepWalk, BERT, and Attention Mechanisms

The paper presents an optimized CTR prediction model for Tencent’s App Store that merges multi‑behavior shared embeddings, long‑term DeepWalk graph embeddings, BERT‑derived app description vectors, and attention‑based fusion, reducing parameters while improving bias, AUC, and recommendation performance for sparse, long‑tail data.

BERTCTR predictionDeepWalk

0 likes · 9 min read

CTR Prediction Optimization for App Store Recommendation: Integrating DeepWalk, BERT, and Attention Mechanisms

DataFunTalk

Jun 13, 2020 · Artificial Intelligence

Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN

This article details how Amap leverages deep‑learning techniques—including temporal and auxiliary feature engineering, multi‑stage RNN models, Wide&Deep architectures, and an Attention‑TCN approach—to accurately identify and handle expired points of interest, improving map freshness and user experience.

Feature EngineeringPOI expirationRNN

0 likes · 13 min read

Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN

Amap Tech

May 8, 2020 · Artificial Intelligence

Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models

The project develops a deep‑learning pipeline for Amap’s expired POI detection that integrates two‑year temporal trend features, industry and verification attributes, a variable‑length LSTM, a Wide‑Deep architecture, and a Wide‑Attention Temporal Convolutional Network, achieving higher accuracy and efficiency while outlining future macro‑and micro‑level enhancements.

Feature EngineeringPOI expirationRNN

0 likes · 15 min read

Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models

Youzan Coder

Oct 25, 2019 · Artificial Intelligence

Personalized Recommendation System Architecture and Techniques at Youzan

Youzan’s personalized recommendation platform combines a four‑layer architecture—data, storage, service, and application—with multi‑dimensional real‑time, offline, and cold‑start recall algorithms, Wide&Deep ranking, HBase/Druid storage, and configurable scene strategies to boost user conversion, traffic monetization, and future scalability.

Big DataCold StartHBase

0 likes · 16 min read

Personalized Recommendation System Architecture and Techniques at Youzan

JD Tech Talk

Jan 10, 2019 · Artificial Intelligence

Sensitive Field Identification Using Wide & Deep and TextCNN Models

This article presents a machine‑learning approach for detecting sensitive data fields in a data warehouse by combining a Wide & Deep network with a TextCNN architecture, detailing data exploration, model design, training strategies, performance results, and deployment workflow.

Feature EngineeringSensitive Data DetectionTextCNN

0 likes · 9 min read

Sensitive Field Identification Using Wide & Deep and TextCNN Models

DataFunTalk

Aug 12, 2018 · Artificial Intelligence

Interpretability of Deep Learning and Low‑Frequency Event Learning in Financial Applications

The article reviews the limitations of mainstream deep‑learning models in finance, proposes hybrid tree‑based and Wide&Deep architectures combined with attention, sensitivity and variance analysis to improve interpretability and low‑frequency event detection, and validates the approach with a large‑scale insurance recommendation case study.

Financeattention mechanismdeep learning

0 likes · 17 min read

Interpretability of Deep Learning and Low‑Frequency Event Learning in Financial Applications

58 Tech

Feb 2, 2018 · Artificial Intelligence

Deep Learning Applications in 58.com Intelligent Recommendation System

This article details how 58.com leverages deep learning models such as FNN, Wide&Deep, CNN+DNN, and YouTube DNN recall, along with a custom AI platform, to enhance recommendation ranking and recall, achieving measurable improvements in click‑through rates and overall system performance.

CNNDNNFNN

0 likes · 13 min read

Deep Learning Applications in 58.com Intelligent Recommendation System

Qunar Tech Salon

Aug 16, 2017 · Artificial Intelligence

Applying Wide & Deep Learning to Meituan‑Dianping Recommendation System

This article describes how Meituan‑Dianping leverages deep learning, especially the Wide & Deep model, to improve its recommendation system by addressing business diversity, user context, feature engineering challenges, optimizer and loss function choices, and presents offline and online experimental results showing significant CTR gains.

Feature Engineeringctrdeep learning

0 likes · 22 min read

Applying Wide & Deep Learning to Meituan‑Dianping Recommendation System