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DataFunTalk
DataFunTalk
Aug 4, 2021 · Artificial Intelligence

Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform

This article presents a comprehensive case study of applying deep learning techniques—including item and user embedding, cross‑domain keyword intent modeling, and multi‑interest representation—to improve the recall stage of personalized recommendation for a cultural‑artifact auction platform, addressing unique data sparsity and diversity challenges.

Deep LearningEmbeddingcross-domain learning
0 likes · 16 min read
Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform
DataFunSummit
DataFunSummit
Aug 3, 2021 · Artificial Intelligence

Content Understanding for Personalized Recommendation: Interest Graph, Concept Mining, and Semantic Matching at Tencent

The article explains how Tencent addresses the limitations of traditional content understanding methods in personalized recommendation by introducing an interest‑graph framework that combines classification, concept, entity, and event layers, and details the associated mining, matching, and online evaluation techniques.

EmbeddingNLPcontent understanding
0 likes · 13 min read
Content Understanding for Personalized Recommendation: Interest Graph, Concept Mining, and Semantic Matching at Tencent
DataFunTalk
DataFunTalk
Aug 2, 2021 · Databases

From Text Search to Vector Search: Generalizing Unstructured Data Retrieval

The article explains why traditional text‑based search engines like ElasticSearch struggle with modern multimodal data, introduces vector databases that store implicit semantic embeddings, and proposes a generalized search architecture that decouples data‑to‑vector mapping from the engine while leveraging clustering or graph indexes for similarity search.

AIEmbeddinginformation retrieval
0 likes · 12 min read
From Text Search to Vector Search: Generalizing Unstructured Data Retrieval
DataFunTalk
DataFunTalk
Jul 3, 2021 · Artificial Intelligence

Knowledge Graph Enhanced Recommender Systems: Methods, Models, and Experiments

This article reviews how knowledge graphs can be integrated into recommender systems to address data sparsity and cold‑start problems, covering collaborative filtering limitations, KG embeddings (TransE, TransH, TransR), deep knowledge‑aware networks, multi‑task feature learning, RippleNet, KGCN, experimental results, and a comparative analysis of performance, scalability, and interpretability.

EmbeddingKnowledge Graphartificial intelligence
0 likes · 11 min read
Knowledge Graph Enhanced Recommender Systems: Methods, Models, and Experiments
DataFunTalk
DataFunTalk
Jul 2, 2021 · Artificial Intelligence

Vector Retrieval for Community Forum Search Using Milvus at Dingxiangyuan

This article describes how Dingxiangyuan's algorithm team adopted Milvus for distributed vector indexing to improve semantic search in their community forum, detailing the background, retrieval workflow, various embedding models—including Bi‑Encoder, Spherical Embedding, and Knowledge Embedding—and summarizing the benefits and future applications.

EmbeddingMilvusNLP
0 likes · 10 min read
Vector Retrieval for Community Forum Search Using Milvus at Dingxiangyuan
DataFunTalk
DataFunTalk
Apr 29, 2021 · Artificial Intelligence

Path‑based Deep Network (PDN) for E‑commerce Recommendation Recall

This paper proposes a Path‑based Deep Network (PDN) that combines similarity‑index and embedding‑based retrieval paradigms to model user‑item interactions via Trigger Net and Similarity Net, achieving significant improvements in click‑through rate, GMV, and diversity on Taobao’s homepage feed.

Deep LearningEmbeddingPDN
0 likes · 21 min read
Path‑based Deep Network (PDN) for E‑commerce Recommendation Recall
DataFunTalk
DataFunTalk
Mar 20, 2021 · Artificial Intelligence

Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications

This article presents a comprehensive technical overview of Momo's model‑based recall system for social recommendation, detailing the underlying user‑scenario behavior models, social graph embeddings, multimodal content semantics, and deployment results that improve matching relevance and user interaction rates.

EmbeddingGraph Neural NetworkMomo
0 likes · 19 min read
Model‑Based Recall in Momo's Social Recommendation: Technical Exploration and Practical Applications
Meituan Technology Team
Meituan Technology Team
Jan 21, 2021 · Mobile Development

Porting Flutter to HarmonyOS: Technical Exploration and Implementation

Meituan’s MTFlutter team rebuilt Flutter’s embedder layer for HarmonyOS by simulating VSync, creating a SurfaceProvider‑based rendering surface, forwarding touch, key and speech events, and re‑implementing asset loading, message loops and lifecycle callbacks, allowing Flutter apps to run on phones, tablets, TVs and wearables.

EmbeddingFlutterHarmonyOS
0 likes · 13 min read
Porting Flutter to HarmonyOS: Technical Exploration and Implementation
DeWu Technology
DeWu Technology
Jan 18, 2021 · Artificial Intelligence

Recall Stage in Recommendation Systems: From Intuition to Deep Learning

The recall stage, the first filtering step after candidate generation, transforms intuitive attribute‑based shortcuts into sophisticated matrix‑factorization and embedding methods—such as dual‑tower and tree‑based models—enabling fast, personalized, diverse candidate selection for real‑time recommendation pipelines.

Deep LearningEmbeddingRecommendation Systems
0 likes · 13 min read
Recall Stage in Recommendation Systems: From Intuition to Deep Learning
DataFunTalk
DataFunTalk
Jan 7, 2021 · Artificial Intelligence

User Preference Mining and Modeling Practices at Beike

This article introduces the concept of user preference mining, discusses challenges such as accurate expression, interpretability, and high-dimensional preferences, reviews statistical and model-based approaches including weighting, decay, XGBoost, DNN, LSTM, Seq4Rec, and Deep Interest Network, and describes their practical implementation at Beike.

BeikeDeep LearningEmbedding
0 likes · 19 min read
User Preference Mining and Modeling Practices at Beike
DataFunTalk
DataFunTalk
Dec 1, 2020 · Artificial Intelligence

A Comprehensive Overview of Embedding Techniques for Recommendation Systems

This article systematically reviews mainstream embedding technologies—including matrix factorization, static and dynamic word embeddings, and graph‑based methods—explaining their principles, implementations, and practical applications in recommendation, advertising, and search systems.

EmbeddingRecommendation Systemsgraph neural networks
0 likes · 32 min read
A Comprehensive Overview of Embedding Techniques for Recommendation Systems
DataFunTalk
DataFunTalk
Nov 28, 2020 · Artificial Intelligence

Building Fast-Iterating Machine Learning Systems at Tubi: A/B Testing, Simple Models, and Embedding Strategies

This article shares Tubi's practical experience in rapidly iterating machine‑learning systems, emphasizing the early importance of simple end‑to‑end A/B testing platforms, clear launch plans, heat‑based and embedding‑based ranking models, and a culture of fast experimentation over complex deep‑learning research.

A/B testingEmbeddingartificial intelligence
0 likes · 8 min read
Building Fast-Iterating Machine Learning Systems at Tubi: A/B Testing, Simple Models, and Embedding Strategies
Bitu Technology
Bitu Technology
Nov 20, 2020 · Artificial Intelligence

Building a Model-Driven Machine Learning System at Tubi: From Simple A/B Tests to Embedding-Based Recommendations

The article shares Tubi's practical experience in building a fast‑iterating machine‑learning platform, emphasizing early measurement, simple end‑to‑end A/B testing, clear launch plans, lightweight popularity and embedding models, and rapid experimentation to drive product decisions.

A/B testingEmbeddingModel Iteration
0 likes · 8 min read
Building a Model-Driven Machine Learning System at Tubi: From Simple A/B Tests to Embedding-Based Recommendations
Sohu Tech Products
Sohu Tech Products
Nov 18, 2020 · Artificial Intelligence

Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms

This article explains the fundamentals of seq2seq neural machine translation models, covering encoder‑decoder architecture, word embeddings, context vectors, RNN processing, and the attention mechanism introduced by Bahdanau and Luong, with visual illustrations and reference links for deeper study.

Deep LearningEmbeddingNeural Machine Translation
0 likes · 11 min read
Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms
58 Tech
58 Tech
Nov 11, 2020 · Artificial Intelligence

Deep Learning for Click‑Through Rate Prediction in 58.com Home‑Page Recommendation

This article details how 58.com leverages deep learning models such as DNN, Wide&Deep, DeepFM, DIN and DIEN, combined with extensive user‑behavior feature engineering, offline vectorization, and online TensorFlow‑Serving pipelines to improve home‑page recommendation click‑through rates and overall platform efficiency.

A/B testingAttention MechanismCTR prediction
0 likes · 25 min read
Deep Learning for Click‑Through Rate Prediction in 58.com Home‑Page Recommendation
DataFunTalk
DataFunTalk
Nov 7, 2020 · Artificial Intelligence

Knowledge Graph Reasoning: Deductive, Inductive, and Embedding‑Based Methods

This article surveys knowledge‑graph reasoning, explaining deductive and inductive reasoning fundamentals, description‑logic and logic‑programming approaches, and modern embedding techniques such as TransE, TransH, TransR and TransD, while highlighting their theoretical bases, practical implementations and recent research progress.

AIEmbeddingKnowledge Graph
0 likes · 13 min read
Knowledge Graph Reasoning: Deductive, Inductive, and Embedding‑Based Methods
DataFunTalk
DataFunTalk
Oct 28, 2020 · Artificial Intelligence

All-Rounder Recall Representation Algorithm Practice

This article presents a comprehensive overview of NetEase Yanxuan’s recall representation algorithms, detailing problem definition, model value, iterative implementations—including session-based embedding, GCN, GraphSAGE, LightGCN, and multi-interest models—along with engineering solutions, performance comparisons, and real-world deployment outcomes in search and recommendation systems.

EmbeddingGraph Neural NetworkSearch
0 likes · 16 min read
All-Rounder Recall Representation Algorithm Practice
DataFunTalk
DataFunTalk
Aug 27, 2020 · Artificial Intelligence

Model Serving in Real-Time: Insights from Alibaba’s User Interest Center

This article explains Alibaba’s User Interest Center approach to real‑time model serving, detailing how it separates offline sequence modeling from lightweight online inference, uses an online interest‑embedding store, and dramatically reduces latency for recommendation models such as DIEN and MIMN.

AlibabaEmbeddingModel Serving
0 likes · 8 min read
Model Serving in Real-Time: Insights from Alibaba’s User Interest Center
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.

CtripDeep LearningEmbedding
0 likes · 24 min read
Hotel Recommendation System Architecture, Models, and Evaluation at Ctrip
DataFunTalk
DataFunTalk
Jul 20, 2020 · Artificial Intelligence

Embedding Techniques in Tencent Mobile News Recommendation System

This article reviews the practical use of embedding technologies in Tencent's mobile news recommendation pipeline, covering the fundamentals of embeddings, their historical development, item and image embeddings, user embeddings, various vector‑based recall methods, clustering strategies, and recent advances and challenges.

Deep LearningEmbeddingTencent
0 likes · 15 min read
Embedding Techniques in Tencent Mobile News Recommendation System
Jike Tech Team
Jike Tech Team
Jul 15, 2020 · Artificial Intelligence

How Embedding-Based Recall Boosted Interaction by 33% in a Live Feed

This article details how Jike's recommendation team upgraded from Spark to TensorFlow, introduced a twin‑tower embedding model for recall, deployed it with TensorFlow Serving and Elasticsearch, and achieved a 33.75% lift in user interaction on the dynamic square.

Deep LearningElasticsearchEmbedding
0 likes · 9 min read
How Embedding-Based Recall Boosted Interaction by 33% in a Live Feed
DataFunTalk
DataFunTalk
Jun 10, 2020 · Artificial Intelligence

Embedding Techniques for Real Estate Recommendation at 58.com

This article explains how 58.com applies various embedding methods—including ALS, Skip‑gram, and DeepWalk—to vectorize users and properties, improve similarity calculations, and enhance both recall and ranking stages of its real‑estate recommendation system, with detailed technical descriptions and evaluation results.

ALSDeepWalkEmbedding
0 likes · 16 min read
Embedding Techniques for Real Estate Recommendation at 58.com
58 Tech
58 Tech
Mar 30, 2020 · Artificial Intelligence

Embedding Techniques for Advertising Recall and Ranking in a Second-Hand Car Platform

This article details the commercial strategy team's exploration of embedding technologies for a second‑hand car platform, covering mainstream embedding methods, their application in advertising recall and ranking pipelines, system architecture, model optimizations, evaluation results, and future directions.

AdvertisingDSSMDeep Learning
0 likes · 22 min read
Embedding Techniques for Advertising Recall and Ranking in a Second-Hand Car Platform
360 Tech Engineering
360 Tech Engineering
Mar 6, 2020 · Fundamentals

Understanding Method Sets, Interfaces, and Embedding in Go

This article explains Go's method sets, the relationship between method receivers and method sets, how interfaces work, and the role of embedding, providing code examples that illustrate value vs pointer receivers, interface implementation rules, and embedding differences for struct types.

EmbeddingInterfacemethod set
0 likes · 10 min read
Understanding Method Sets, Interfaces, and Embedding in Go
Qunar Tech Salon
Qunar Tech Salon
Feb 6, 2020 · Artificial Intelligence

Content Understanding for Personalized Feed Recommendation: From Classification to Interest Graphs

The article explains how Tencent tackles content understanding in feed recommendation by evolving from traditional classification, keyword, and entity methods to a multi‑layer interest graph that captures concepts and events, addressing the need for full context, reasoning about user intent, and improving online performance.

AIEmbeddingNLP
0 likes · 12 min read
Content Understanding for Personalized Feed Recommendation: From Classification to Interest Graphs
Aotu Lab
Aotu Lab
Dec 5, 2019 · Databases

Mastering One-to-N Relationships in MongoDB: Practical Design Patterns and Tips

This multi‑part guide explains how to model One‑to‑N relationships in MongoDB, covering basic patterns for one‑to‑few, one‑to‑many, and one‑to‑squillions, then advancing to two‑way referencing and denormalization, and finally offering a concise set of rules of thumb for choosing the right schema design.

DenormalizationEmbeddingMongoDB
0 likes · 21 min read
Mastering One-to-N Relationships in MongoDB: Practical Design Patterns and Tips
Tencent Cloud Developer
Tencent Cloud Developer
Dec 3, 2019 · Artificial Intelligence

Feature Engineering Practices for Short‑Video Recommendation Systems

Effective short‑video recommendation relies on meticulous feature engineering that transforms raw signals—numerical counts, categorical IDs, content and user embeddings, context and session data—through bucketization, scaling, crossing, and smoothing, then selects and evaluates them via filtering, wrapping, regularization, and importance analysis to mitigate business biases and improve multi‑objective ranking performance.

Embeddingbias mitigationdata preprocessing
0 likes · 32 min read
Feature Engineering Practices for Short‑Video Recommendation Systems
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 24, 2019 · Artificial Intelligence

Unlocking Better Knowledge Graph Reasoning: The CrossE Model Explained

CrossE introduces an explicit crossover interaction mechanism for knowledge graph embedding, learning both general and interaction-specific representations of entities and relations, which improves link prediction accuracy and provides interpretable explanations, as demonstrated on benchmark datasets WN18, FB15k, and FB15k-237.

EmbeddingInterpretabilityKnowledge Graph
0 likes · 9 min read
Unlocking Better Knowledge Graph Reasoning: The CrossE Model Explained
DataFunTalk
DataFunTalk
Jun 25, 2019 · Artificial Intelligence

Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms

This article describes how Tujia applied embedding techniques, particularly a Skip‑Gram model, to build an item‑to‑item similarity recommender for low‑frequency, highly personalized homestay listings, detailing the data preparation, model architecture, training process, evaluation results, practical improvements, and future directions.

AB testEmbeddinghomestay
0 likes · 13 min read
Embedding‑Based Item‑to‑Item Recommendation for Homestay Platforms
Youku Technology
Youku Technology
Apr 22, 2019 · Artificial Intelligence

Exploring the Construction of an Entertainment Brain: AI and Big Data Practices in the Fish Brain Platform

The talk introduces Alibaba’s Fish Brain platform, an AI‑powered decision‑support system for entertainment that combines a three‑layer data‑model, AI‑processed basic data, and application models, leveraging NLP, computer‑vision, custom embeddings, loss functions and predictive hybrid networks to analyze content, user behavior, and forecast performance.

AIBig DataEmbedding
0 likes · 12 min read
Exploring the Construction of an Entertainment Brain: AI and Big Data Practices in the Fish Brain Platform
NetEase Game Operations Platform
NetEase Game Operations Platform
Mar 27, 2019 · Big Data

Embedding Python in Java with Jython for Real‑Time Big Data Jobs

This article explains why and how to embed Python code in Java using Jython for real‑time big‑data processing, covering performance benefits, memory‑leak pitfalls, singleton interpreter patterns, function factories, Java‑object conversion, and importing external PyPI packages with practical code examples.

Big DataDynamic LanguageEmbedding
0 likes · 11 min read
Embedding Python in Java with Jython for Real‑Time Big Data Jobs
DataFunTalk
DataFunTalk
Mar 19, 2019 · Artificial Intelligence

Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems

This article explores how Field-aware Factorization Machines (FFM) can be employed to replace multi‑path recall strategies in industrial recommendation systems, detailing model principles, embedding construction, integration of user, item and context features, performance considerations, and potential for unifying recall and ranking stages.

EmbeddingFFMRecommendation Systems
0 likes · 51 min read
Using Field-aware FM (FFM) Models for Unified Recall in Recommendation Systems
Sohu Tech Products
Sohu Tech Products
Mar 6, 2019 · Artificial Intelligence

Applying Word2Vec Embeddings to Rental and News Recommendation: Model, Hyper‑parameters, and Optimization

This article explains the fundamentals of the Word2Vec SGNS model, details its hyper‑parameters and training tricks, and demonstrates how customized embeddings are built for rental‑listing and news‑article recommendation, covering data preparation, objective‑function redesign, evaluation, and deployment in both recall and ranking stages.

EmbeddingSGNSWord2Vec
0 likes · 14 min read
Applying Word2Vec Embeddings to Rental and News Recommendation: Model, Hyper‑parameters, and Optimization
DataFunTalk
DataFunTalk
Jan 8, 2019 · Artificial Intelligence

Yoo Video Bottom‑Page Recommendation System: From Zero to One Practice

This article details the end‑to‑end design, recall and ranking techniques, engineering implementation, and future research directions of Tencent's Yoo video bottom‑page recommendation system, illustrating how large‑scale video recommendation is built from business needs to deep learning models.

Embeddinglarge-scale systemsmachine learning
0 likes · 13 min read
Yoo Video Bottom‑Page Recommendation System: From Zero to One Practice
JavaScript
JavaScript
Nov 3, 2017 · Frontend Development

How to Embed Web Pages in Mini-Programs Using the Web-View Component

Developers can now flexibly embed web pages within mini-programs using the web-view component, which fills the entire page as a container, though it currently excludes personal and overseas mini-program types, and the guide shows the required WXML markup to set the source URL.

EmbeddingWeChatfrontend
0 likes · 1 min read
How to Embed Web Pages in Mini-Programs Using the Web-View Component