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Architecture Digest
Architecture Digest
Feb 8, 2022 · Artificial Intelligence

Design and Evolution of a Scalable Recommendation System Architecture (V1.0‑V3.0)

This article describes the progressive redesign of an e‑commerce recommendation platform—from a simple strategy‑factory V1.0 through a vertically split V2.0 to a fully configurable, pipeline‑driven V3.0—highlighting architectural challenges, Redis clustering, dynamic configuration, recall and prediction services, and future directions for fine‑grained, explainable recommendations.

AIDynamic ConfigurationPipeline
0 likes · 13 min read
Design and Evolution of a Scalable Recommendation System Architecture (V1.0‑V3.0)
DeWu Technology
DeWu Technology
Feb 7, 2022 · Artificial Intelligence

Generalized Recommendation Solution for Transaction Scenarios

DeWu’s e‑commerce platform consolidated dozens of small‑scale transaction scenes into a universal personalized recommendation system by adopting a user‑to‑item DSSM dual‑tower model with unified sampling, category‑aware negative mining, cosine‑normalized embeddings, and real‑time serving, boosting click‑through rates by over 10% across all scenarios.

DSSMdual-towere‑commerce
0 likes · 13 min read
Generalized Recommendation Solution for Transaction Scenarios
Xianyu Technology
Xianyu Technology
Jan 29, 2022 · Artificial Intelligence

Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search

Xianyu search recall stage upgraded from simple text matching to semantic vector retrieval using DSSM with Electra‑Small, query graph attention, and behavior‑based I2I, adding structured attributes and OCR tags, improving AUC to 0.824 and HitRate@10 to 90.1%, boosting purchase metrics by up to 4%.

Deep LearningVector RetrievalXianyu
0 likes · 17 min read
Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search
DataFunTalk
DataFunTalk
Jan 23, 2022 · Artificial Intelligence

Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation

This article presents a systematic study of recall techniques for new‑user cold‑start in content recommendation, describing a baseline two‑tower model, a Dual Attention Network (DAN) fusion approach, and an enhanced Contextual‑Gate DAN that dynamically balances content and product sequences, together with offline and online evaluation results and future directions.

Deep LearningUser Embeddingcold-start
0 likes · 12 min read
Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation
JD Retail Technology
JD Retail Technology
Dec 20, 2021 · Artificial Intelligence

Large-Scale Graph Technology in JD.com E‑commerce: Practice and AI Computing Directions

The article summarizes JD.com Vice President Bao Yongjun's presentation on applying ultra‑large‑scale graph technology to e‑commerce, covering data foundations, recommendation and fraud detection use cases, technical challenges, the Galileo graph engine, and future AI computing development directions such as chips, auto‑learning, application layers, and privacy protection.

e‑commercefraud detectiongraph computing
0 likes · 7 min read
Large-Scale Graph Technology in JD.com E‑commerce: Practice and AI Computing Directions
Alimama Tech
Alimama Tech
Dec 8, 2021 · Artificial Intelligence

Dual Vector Foil (DVF): Decoupled Index and Model Retrieval for Large-Scale Recall

The Dual Vector Foil (DVF) system decouples index construction from model training by building a post‑training HNSW graph, enabling any complex model to score candidates, which yields a 5.7 % recall boost, cuts latency from ~40 ms to 6.5 ms, and raises QPS over tenfold while simplifying maintenance.

dual vector foilindexinglarge-scale retrieval
0 likes · 27 min read
Dual Vector Foil (DVF): Decoupled Index and Model Retrieval for Large-Scale Recall
58 Tech
58 Tech
Nov 25, 2021 · Artificial Intelligence

Technical Evolution of the “Guess You Want” Recommendation Module in 58 Local Services

This article describes the design, multi‑stage recall strategies, and successive ranking model upgrades—including BERT‑based intent prediction, vector‑based DSSM recall, tag expansion, and multi‑task DeepFM/MMoE/ESMM architectures—that together reduce no‑result rates and significantly improve user conversion for 58's local service platform.

BERTDSSMmulti-task learning
0 likes · 16 min read
Technical Evolution of the “Guess You Want” Recommendation Module in 58 Local Services
DataFunSummit
DataFunSummit
Nov 19, 2021 · Artificial Intelligence

Sliding Spectrum Decomposition (SSD) for Diversified Recommendation in Re‑ranking

This article reviews the Sliding Spectrum Decomposition (SSD) model presented by Xiaohongshu at KDD 2021, explaining how it incorporates sliding‑window diversity into the re‑ranking stage, combines content‑based and collaborative‑filtering embeddings via the CB2CF framework, and demonstrates its effectiveness through offline and online A/B experiments.

DiversityEmbeddingSSD
0 likes · 14 min read
Sliding Spectrum Decomposition (SSD) for Diversified Recommendation in Re‑ranking
DataFunSummit
DataFunSummit
Nov 7, 2021 · Artificial Intelligence

How Information‑Flow Recommendation Systems Upgrade Drives User Growth

The article examines how low‑level recommendation‑algorithm improvements in information‑flow feeds can boost user retention, LTV and overall growth by addressing cold‑start challenges, survivor bias, and causal inference through personalized ranking, ecosystem construction, and multi‑task learning.

Information Flowalgorithmcausal inference
0 likes · 14 min read
How Information‑Flow Recommendation Systems Upgrade Drives User Growth
DataFunSummit
DataFunSummit
Oct 31, 2021 · Artificial Intelligence

Exploring Generalized Multi‑Objective Recommendation Algorithms for 58 Community

This article details how 58 Community evolved its recommendation system from single‑objective click‑rate optimization to a multi‑objective framework that boosts value‑content share, improves user retention, and leverages cross‑domain embeddings and online CEM‑based parameter tuning to achieve significant performance gains.

CEMEmbeddingOnline Optimization
0 likes · 15 min read
Exploring Generalized Multi‑Objective Recommendation Algorithms for 58 Community
DataFunSummit
DataFunSummit
Oct 10, 2021 · Artificial Intelligence

Advances in Knowledge Graph Construction and Applications at Alibaba's AliMe

This article presents Alibaba's AliMe team’s year‑long progress on knowledge graphs, covering the basics of knowledge graphs, domain‑specific and multi‑modal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and key takeaways for future research.

Artificial Intelligenceentity extractione‑commerce
0 likes · 24 min read
Advances in Knowledge Graph Construction and Applications at Alibaba's AliMe
DataFunTalk
DataFunTalk
Oct 4, 2021 · Artificial Intelligence

Exploring Multi-Objective Recommendation Algorithms for 58 Community: Cross-Domain Embedding and Online Optimization

This article details how 58 Community improved content value share, click‑through, and user retention by designing a generalized multi‑objective recommendation algorithm that leverages cross‑domain embeddings, DeepFM‑DIN models, EGES‑inspired pre‑training, and online CEM‑based parameter optimization.

CEMDeep LearningUser Retention
0 likes · 16 min read
Exploring Multi-Objective Recommendation Algorithms for 58 Community: Cross-Domain Embedding and Online Optimization
DataFunTalk
DataFunTalk
Sep 30, 2021 · Artificial Intelligence

Advances in Knowledge Graph Construction and Applications by Alibaba's AliMe Team

This article presents Alibaba's AliMe team's year‑long progress on knowledge graph research, covering the fundamentals of knowledge graphs, domain and multimodal graph construction techniques, practical e‑commerce applications such as dialogue‑driven recommendation, virtual‑anchor script generation, and insights on future directions.

AIMultimodaldialogue system
0 likes · 23 min read
Advances in Knowledge Graph Construction and Applications by Alibaba's AliMe Team
DataFunSummit
DataFunSummit
Aug 29, 2021 · Artificial Intelligence

Zhihu Recommendation Page Ranking: Architecture, Feature Design, Model Evolution, and Practical Insights

This article presents a comprehensive overview of Zhihu's recommendation page ranking system, detailing the request flow, ranking evolution from time‑based to deep‑learning models, feature engineering strategies, model architectures such as DNN, DeepFM, DIN, multi‑task learning, and lessons learned for production deployment.

CTRfeature engineeringmachine learning
0 likes · 12 min read
Zhihu Recommendation Page Ranking: Architecture, Feature Design, Model Evolution, and Practical Insights
DataFunSummit
DataFunSummit
Aug 14, 2021 · Artificial Intelligence

Intelligent Recommendation System Architecture and Flowengine Governance

This article examines the evolving landscape of recommendation systems, outlines current business trends and technical challenges, and introduces Flowengine—a declarative, low‑code, component‑based framework that improves architecture governance, scalability, and operational efficiency for AI‑driven recommendation services.

AIFlowenginearchitecture
0 likes · 21 min read
Intelligent Recommendation System Architecture and Flowengine Governance
DataFunSummit
DataFunSummit
Aug 10, 2021 · Artificial Intelligence

Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding

The article examines the rapid growth of recommendation systems, highlighting the need for industrial‑grade benchmarks, transparent explainability, and addressing algorithmic confounding caused by feedback loops, while discussing how these issues affect both users and content providers in the AI‑driven ecosystem.

AIFeedback Loopbenchmark
0 likes · 12 min read
Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding
DataFunSummit
DataFunSummit
Aug 8, 2021 · Artificial Intelligence

Diversity as a Means, Not an End, in Recommendation Systems

The article argues that diversity in recommendation systems should be treated as a means rather than an ultimate goal, explains why it is hard to quantify, suggests using real performance metrics such as click‑through rate and dwell time, and offers practical strategies to improve listwise ranking.

DiversityMetricslistwise
0 likes · 7 min read
Diversity as a Means, Not an End, in Recommendation Systems
DataFunSummit
DataFunSummit
Aug 5, 2021 · Artificial Intelligence

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

This article describes how Tujia applied embedding techniques, inspired by word2vec and skip‑gram models, to build item‑to‑item similarity vectors for homestay recommendations, detailing the background challenges, the embedding solution, training methodology, evaluation results, practical improvements, and future development plans.

AB testingEmbeddinghomestay
0 likes · 13 min read
Embedding‑Based Item‑to‑Item Similarity Recommendation for Homestay Platforms
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
DataFunSummit
DataFunSummit
Jul 26, 2021 · Artificial Intelligence

Deep Learning Ranking System and Model for NetEase News Feed Personalization

This article presents the design, implementation, and optimization of a deep‑learning‑based ranking system for NetEase News, covering pipeline architecture, feature‑processing enhancements, custom TensorFlow operators, and modular model frameworks such as DCN and DIEN to improve recommendation performance.

AIPipelinefeature engineering
0 likes · 11 min read
Deep Learning Ranking System and Model for NetEase News Feed Personalization
DataFunTalk
DataFunTalk
Jul 24, 2021 · Artificial Intelligence

Instant Interest Reinforcement and Extension for Taobao Detail Page Distribution

This article presents the mechanisms of Taobao’s detail‑page full‑network distribution, introducing background, scenario description, and a series of algorithmic explorations—including CIDM, DTIN, and Tri‑tower models—that leverage the main product (trigger) to reinforce users’ instant interests, improve recall, coarse‑ranking, and fine‑ranking performance, and achieve notable online metric gains.

CTRDeep LearningModeling
0 likes · 17 min read
Instant Interest Reinforcement and Extension for Taobao Detail Page Distribution
Meituan Technology Team
Meituan Technology Team
Jul 15, 2021 · Artificial Intelligence

Local Life Comprehensive Demand Knowledge Graph: Design, Algorithms, and Applications

The Local Life Comprehensive Demand Knowledge Graph (GENE) reorients Meituan’s supply‑demand matching by building a multi‑layer, user‑centric graph that captures intent and consideration, employing BERT, Word2Vec, ELECTRA, and reinforcement‑learning models to generate concrete and scene‑based demand nodes, now powering parent‑child, leisure, medical‑beauty, and education services.

AIDemand ModelingNLP
0 likes · 34 min read
Local Life Comprehensive Demand Knowledge Graph: Design, Algorithms, and Applications
Xianyu Technology
Xianyu Technology
Jul 15, 2021 · Backend Development

HermesX: A Unified Push Notification Platform for Xianyu

HermesX unifies Xianyu’s fragmented push pipelines into a single flow, optimizes content relevance, applies a four‑layer fatigue model, and offers a low‑code management UI, delivering up to 35% less abnormal traffic, 65% lower compute use, and 69% reduced peak latency while paving the way for richer content pools and finer user segmentation.

Push NotificationSystem Architectureperformance optimization
0 likes · 12 min read
HermesX: A Unified Push Notification Platform for Xianyu
58 Tech
58 Tech
Jul 7, 2021 · Artificial Intelligence

Multi‑Objective Modeling for CRM Opportunity Allocation: Iterative Deep Learning Approaches

This article details the development and iterative optimization of multi‑task deep learning models—including XGBoost‑based baselines, MMoE, ESMM‑enhanced MMoE, PLE, and bias‑aware ranking—to simultaneously improve call‑out and connect‑out rates in a CRM opportunity distribution system, presenting offline gains and online deployment results for each version.

CRMModel Optimizationmulti-task learning
0 likes · 33 min read
Multi‑Objective Modeling for CRM Opportunity Allocation: Iterative Deep Learning Approaches
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
Jun 4, 2021 · Artificial Intelligence

Advances in Ranking Algorithms for the "Good Goods" Recommendation Scenario

This article presents a comprehensive overview of recent advancements in ranking algorithms for the Good Goods recommendation scenario, covering long‑sequence modeling, category‑retrieval attention, multi‑objective ranking, model structure optimizations, loss functions, and LTR techniques, along with experimental results and practical insights.

LTRModel Optimizationattention
0 likes · 13 min read
Advances in Ranking Algorithms for the "Good Goods" Recommendation Scenario
DataFunTalk
DataFunTalk
May 15, 2021 · Artificial Intelligence

Multi‑Interest Recall Techniques in iQIYI Short‑Video Recommendation

The article reviews the evolution of iQIYI's short‑video recommendation recall pipeline, detailing multi‑interest recall methods such as clustering‑based recall, MOE‑based recall, single‑activation multi‑interest networks, regularization strategies, dynamic capacity handling, and multimodal extensions, and discusses their impact on recommendation performance.

TransformeriQIYImachine learning
0 likes · 15 min read
Multi‑Interest Recall Techniques in iQIYI Short‑Video Recommendation
JD Tech
JD Tech
Apr 30, 2021 · Artificial Intelligence

Smart DMP: A Next‑Generation Intelligent Targeting System for E‑commerce Advertising

This article reviews the limitations of traditional DMP and AI‑driven intelligent targeting in e‑commerce, introduces JD.com's Smart DMP framework that combines merchant intent with high‑relevance modeling, and presents experimental results showing over 15% CTR improvement and widespread merchant adoption.

AIAdvertisingSmart DMP
0 likes · 9 min read
Smart DMP: A Next‑Generation Intelligent Targeting System for E‑commerce Advertising
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
iQIYI Technical Product Team
iQIYI Technical Product Team
Apr 23, 2021 · Artificial Intelligence

How iQIYI’s Multi‑Interest Recall Transforms Video Recommendation

This article analyzes iQIYI’s evolution of multi‑interest recall techniques—from clustering‑based PinnerSage to MOE and single‑activation models—showing how extracting multiple user interests improves recall diversity, mitigates filter bubbles, and boosts key performance metrics in short‑video recommendation.

iQIYImachine learningmulti-interest recall
0 likes · 16 min read
How iQIYI’s Multi‑Interest Recall Transforms Video Recommendation
Xianyu Technology
Xianyu Technology
Apr 21, 2021 · Backend Development

Seller Posting Promotion Platform Architecture and Implementation

To boost Xianyu’s user retention, the team built a long‑term promotion platform that combines configurable operational activities with algorithmic SPU recommendations, using Kunpeng extension points, supply‑demand analysis, and conditional search to personalize seller prompts, improve click‑through, and lay groundwork for broader scenario expansion.

algorithmdata-analysise‑commerce
0 likes · 9 min read
Seller Posting Promotion Platform Architecture and Implementation
DataFunTalk
DataFunTalk
Apr 17, 2021 · Artificial Intelligence

Personalized Re-ranking for Recommendation (ResSys'19)

This article introduces a personalized re‑ranking model for recommendation systems, explaining the limitations of traditional point‑wise ranking, describing the PRM architecture with input, encoding, and output layers using multi‑head attention and pre‑trained personalization features, and presenting experimental results and future extensions.

CTRTransformerattention
0 likes · 7 min read
Personalized Re-ranking for Recommendation (ResSys'19)
58 Tech
58 Tech
Apr 12, 2021 · Artificial Intelligence

Deep Interest Modeling and Multi‑Channel Recommendation for 58.com Home Page

This article presents the challenges of large‑scale home‑page recommendation at 58.com, describes how behavior‑sequence models such as DIN, DIEN and Transformer are applied and evolved into double‑channel and multi‑channel deep interest architectures, and details offline and online performance optimizations that yielded significant gains in click‑through and conversion rates.

AISequence Modelinglarge-scale systems
0 likes · 19 min read
Deep Interest Modeling and Multi‑Channel Recommendation for 58.com Home Page
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
DeWu Technology
DeWu Technology
Mar 12, 2021 · Industry Insights

How Do Recommendation Systems Rank Items? A Deep Dive into Models and Strategies

This article explains the architecture and ranking process of modern recommendation systems, covering the two-stage pipeline of candidate generation and ranking, the evolution from rule‑based methods to logistic regression, GBDT, wide‑and‑deep, and deep learning models, and discusses challenges such as feature non‑linearity, multi‑objective optimization, and the need for post‑ranking interventions.

Deep LearningGBDTIndustry Insights
0 likes · 15 min read
How Do Recommendation Systems Rank Items? A Deep Dive into Models and Strategies
21CTO
21CTO
Mar 11, 2021 · Artificial Intelligence

Why Search Engines Are Evolving Beyond Traffic Gateways

The article analyzes how search traffic has shifted from Google dominance to a balanced ecosystem with Facebook, mobile content platforms, and AI-driven recommendation, highlighting the rise of omnichannel "full‑stack" search that integrates content, services, and emerging technologies.

AIMobilecontent
0 likes · 14 min read
Why Search Engines Are Evolving Beyond Traffic Gateways
DataFunTalk
DataFunTalk
Mar 4, 2021 · Artificial Intelligence

Interactive Recommendation and Travel Theme Recommendation in the Fliggy App

This article presents the design and implementation of interactive recommendation and travel‑theme recommendation in Alibaba's Fliggy app, covering background, user demand classification, real‑time interest capture, various recall strategies, ranking models, multi‑task learning, and engineering tricks to improve CTR and user experience.

AIFliggyinteractive recommendation
0 likes · 16 min read
Interactive Recommendation and Travel Theme Recommendation in the Fliggy App
AntTech
AntTech
Mar 3, 2021 · Artificial Intelligence

Ant Group Intelligent Service Research Overview: NLP, Dialogue, Recommendation, and Anti‑fraud Papers

The article presents a comprehensive overview of Ant Group's intelligent service research, summarizing recent AI‑focused papers on text classification, stance detection, data augmentation, knowledge distillation for ranking, reinforcement‑learning‑based dialogue clarification, behavior‑cloning dialogue systems, anti‑fraud outbound bots, tag‑based service recommendation, and multi‑agent service groups, while also highlighting future directions and recruitment opportunities.

AI researchAnti‑fraudDialogue Systems
0 likes · 17 min read
Ant Group Intelligent Service Research Overview: NLP, Dialogue, Recommendation, and Anti‑fraud Papers
21CTO
21CTO
Feb 26, 2021 · Artificial Intelligence

Why One Metric Isn't Enough: Multi‑Dimensional Evaluation of Recommendation Systems

The article explains why relying on a single metric like click‑through rate is insufficient for recommendation systems, and outlines a comprehensive, multi‑dimensional evaluation framework that combines business indicators, user behavior metrics, and algorithmic performance measures such as recall, precision, and AUC.

AB testingAIAUC
0 likes · 10 min read
Why One Metric Isn't Enough: Multi‑Dimensional Evaluation of Recommendation Systems
DataFunTalk
DataFunTalk
Feb 25, 2021 · Artificial Intelligence

Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community

This article describes how a UGC app tackled user and content cold‑start problems by introducing a personalized vector‑recall pipeline based on network representation learning and multimodal embeddings, detailing graph construction, GraphSAGE and GAT implementations, offline experiments, A/B test results, and future directions.

GNNMultimodalgraph-embedding
0 likes · 14 min read
Applying Graph Embedding and Vector Recall for Personalized Recommendation in a UGC Community
Alibaba Cloud Developer
Alibaba Cloud Developer
Feb 24, 2021 · Artificial Intelligence

How Alibaba’s ICBU Algorithm Team Transformed E‑Commerce in 2020

This article reviews the 2020 achievements of Alibaba.com’s ICBU algorithm team, explaining the evolving role of algorithm engineers, the fundamentals of e‑commerce algorithms, the team’s three‑pillar workflow of Understanding, Growth, and Matching, and the technical breakthroughs that drove business impact and future directions.

Alibabaalgorithme‑commerce
0 likes · 28 min read
How Alibaba’s ICBU Algorithm Team Transformed E‑Commerce in 2020
DataFunTalk
DataFunTalk
Feb 24, 2021 · Artificial Intelligence

Multi‑Objective Ranking in Kuaishou Short‑Video Recommendation: System Design and Online Results

This article details Kuaishou's multi‑objective ranking pipeline for short‑video recommendation, covering manual score fusion, GBDT ensemble, Learn‑to‑Rank, online auto‑tuning, ensemble sorting, reinforcement‑learning rerank, and on‑device rerank, and reports their impact on DAU, watch time and user interaction.

KuaishouReinforcement Learningmachine learning
0 likes · 21 min read
Multi‑Objective Ranking in Kuaishou Short‑Video Recommendation: System Design and Online Results
DataFunTalk
DataFunTalk
Feb 15, 2021 · Artificial Intelligence

Deep Tree Matching (TDM): Evolution and Practice in Large-Scale Retrieval at Alibaba

This article explains Alibaba's Deep Tree Matching (TDM) technology, covering the challenges of large‑scale match retrieval, the progression from classic two‑stage recall to tree‑based indexing, max‑heap tree modeling, beam‑search retrieval, and the joint model‑index learning across TDM 1.0, 2.0, and 3.0, highlighting significant offline and online performance gains and future research directions.

AlibabaBeam SearchDeep Learning
0 likes · 15 min read
Deep Tree Matching (TDM): Evolution and Practice in Large-Scale Retrieval at Alibaba
58 Tech
58 Tech
Jan 8, 2021 · Artificial Intelligence

Deep Learning Practices for Multi‑Business Integrated Recommendation: From Dual‑Channel to Multi‑Channel Interest Models and Multi‑Scenario Adaptation

The article details how 58.com tackled the challenges of multi‑business recommendation by evolving its ranking models from a dual‑channel deep interest architecture to a 1+N multi‑channel deep interest model, incorporating customized feature cross layers, scenario‑adaptation mechanisms, and extensive engineering optimizations that yielded significant CTR and conversion gains.

Multi‑Channelfeature engineeringinterest modeling
0 likes · 27 min read
Deep Learning Practices for Multi‑Business Integrated Recommendation: From Dual‑Channel to Multi‑Channel Interest Models and Multi‑Scenario Adaptation
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Jan 8, 2021 · Artificial Intelligence

Design and Implementation of a Rule‑Based and Collaborative‑Filtering Recommendation System for an Educational App

This article describes the business background, cold‑start challenges, rule‑based recall pipeline, Wilson interval and time‑decay scoring methods, item‑based collaborative filtering implementation with code, and experimental results that improved click‑through rates for the 学而思网校 educational application.

A/B-testWilson-intervalcold-start
0 likes · 12 min read
Design and Implementation of a Rule‑Based and Collaborative‑Filtering Recommendation System for an Educational App
DataFunTalk
DataFunTalk
Jan 8, 2021 · Artificial Intelligence

Deconstructing E‑commerce Recommendation Systems: Architecture, Challenges, and Strategies

This article provides a comprehensive overview of e‑commerce recommendation systems, detailing their end‑to‑end workflow, key challenges such as multi‑scenario objectives and data loops, core components like recall and ranking, model evolution, feature engineering, evaluation metrics, and practical considerations for building a healthy, multi‑objective recommendation ecosystem.

e‑commercemachine learningpersonalization
0 likes · 17 min read
Deconstructing E‑commerce Recommendation Systems: Architecture, Challenges, and Strategies
DataFunTalk
DataFunTalk
Nov 30, 2020 · Fundamentals

DataFunTalk Annual Conference – Full Program and Speaker Details

The DataFunTalk year‑end conference will be held online on December 19‑20, featuring over 90 speakers across multiple forums covering recommendation algorithms, knowledge graphs, AI, big data, security, and product development, with detailed session schedules, speaker bios, and registration information.

AIBig DataProduct Development
0 likes · 76 min read
DataFunTalk Annual Conference – Full Program and Speaker Details
JD Cloud Developers
JD Cloud Developers
Nov 4, 2020 · Artificial Intelligence

How Cloud Trade Fairs Use AI to Power Smart Recommendations

This article explains how a cloud‑based trade fair leverages AI techniques—including user and item profiling, multi‑level caching with Caffeine and Redis, and a Deep Interest Network model with attention mechanisms—to deliver personalized, high‑performance recommendations for exhibitors, buyers, and individual users.

AIDeep Learningcaching
0 likes · 15 min read
How Cloud Trade Fairs Use AI to Power Smart Recommendations
Beike Product & Technology
Beike Product & Technology
Oct 29, 2020 · Artificial Intelligence

Engineering Architecture Practices for an AI‑Powered Recommendation Platform at Beike

The article details Beike's intelligent recommendation platform, describing its C‑end and B‑end user scenarios, the challenges of handling numerous recommendation scenes and material types, and how a strategy‑driven, multi‑stage architecture—from rapid V1.0 construction to V4.0 deep‑model integration—has been evolved, optimized for stability, real‑time processing, and future search‑recommendation convergence.

AIBeikearchitecture
0 likes · 17 min read
Engineering Architecture Practices for an AI‑Powered Recommendation Platform at Beike
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 Networkmachine learning
0 likes · 16 min read
All-Rounder Recall Representation Algorithm Practice
DataFunTalk
DataFunTalk
Oct 23, 2020 · Artificial Intelligence

Feedback‑Aware Deep Matching Model for Music Recommendation in Tmall Genie

This article presents DeepMatch, a behavior‑sequence based deep learning recall model enhanced with play‑rate and intent‑type embeddings, describes its self‑attention architecture, factorized embedding parameterization, multitask loss design, distributed TensorFlow training tricks, and demonstrates significant offline and online improvements in music recommendation performance.

Deep LearningSelf-AttentionTensorFlow
0 likes · 15 min read
Feedback‑Aware Deep Matching Model for Music Recommendation in Tmall Genie
DataFunTalk
DataFunTalk
Sep 29, 2020 · Artificial Intelligence

Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems

This article introduces the Deep Sparse Network (NON), a new deep neural architecture for recommendation systems that combines field‑wise networks, across‑field interaction networks, and an operation‑fusion network, and demonstrates its superior performance through extensive experiments and ablation studies.

CTR predictionDeep Learningfeature interaction
0 likes · 14 min read
Deep Sparse Network (NON): A Novel Deep Neural Network Model for Recommendation Systems
DataFunTalk
DataFunTalk
Sep 19, 2020 · Artificial Intelligence

AliCoCo: Alibaba’s E‑commerce Cognitive Concept Net – Architecture, Construction, and Applications

The article presents AliCoCo, Alibaba’s large‑scale e‑commerce knowledge graph that models user demand as concepts, describes its four‑layer architecture, the algorithms for concept extraction, taxonomy building, and item association, and demonstrates its impact on search and recommendation systems.

AlibabaNLPconcept extraction
0 likes · 22 min read
AliCoCo: Alibaba’s E‑commerce Cognitive Concept Net – Architecture, Construction, and Applications
58 Tech
58 Tech
Sep 7, 2020 · Artificial Intelligence

Optimizing Individual Diversity in Recommendation Systems: Architecture, MMR and DPP Implementation at 58 Tribe

This article presents a comprehensive study on improving individual diversity in recommendation systems by detailing architectural optimizations across recall, rule, and re‑ranking layers, explaining the principles and practical deployment of MMR and DPP algorithms, and demonstrating their impact on key business metrics through extensive experiments.

Algorithm OptimizationCustom DistanceDPP
0 likes · 18 min read
Optimizing Individual Diversity in Recommendation Systems: Architecture, MMR and DPP Implementation at 58 Tribe
DataFunTalk
DataFunTalk
Sep 4, 2020 · Artificial Intelligence

Beam Search Aware Training for Optimal Tree-Based Retrieval Models

This article presents a comprehensive study of tree-based deep models for large-scale matching, introduces the theoretical framework of optimal tree models, proposes a Beam Search aware training algorithm (BSAT/OTM) to address training-test mismatch, and demonstrates significant recall improvements on Amazon Books and UserBehavior datasets.

Beam SearchDeep Learninglarge-scale matching
0 likes · 23 min read
Beam Search Aware Training for Optimal Tree-Based Retrieval Models
Xianyu Technology
Xianyu Technology
Sep 1, 2020 · Artificial Intelligence

Interest-Based Live Stream Recommendation System for Xianyu

Within three weeks, the team built an interest‑based live‑stream recommendation platform for Xianyu that combined operational insights, BI analysis, and offline algorithms to generate user‑anchor interest tags, sync them to an online graph, and dramatically boost top‑room UV and click‑through rates.

Big DataGraph Databaseinterest tagging
0 likes · 8 min read
Interest-Based Live Stream Recommendation System for Xianyu
DataFunTalk
DataFunTalk
Aug 28, 2020 · Artificial Intelligence

Intelligent Traffic Distribution in 58 Local Services: Algorithmic Practices and System Optimization

This article presents a comprehensive overview of 58 Local Services' traffic distribution system, detailing the ecosystem, user interaction flow, challenges such as information homogeneity and complex user structures, and the algorithmic solutions—including information and knowledge structuring, multi‑task user intent modeling, layered optimization, and system integration—used to improve recall, ranking, and real‑time personalization.

AIinformation structuringmulti-task learning
0 likes · 21 min read
Intelligent Traffic Distribution in 58 Local Services: Algorithmic Practices and System Optimization
DataFunTalk
DataFunTalk
Aug 20, 2020 · Artificial Intelligence

Weibo Recommendation Algorithm Practice and Machine Learning Platform Evolution

This article shares Weibo’s experience in building and evolving its recommendation algorithms, covering the recommendation scenario, machine learning workflow, feature engineering, model upgrades, large‑scale challenges, deployment via the Weiflow platform, and the capabilities of its machine‑learning infrastructure.

Online LearningWeibofeature engineering
0 likes · 14 min read
Weibo Recommendation Algorithm Practice and Machine Learning Platform Evolution
Xianyu Technology
Xianyu Technology
Aug 20, 2020 · Artificial Intelligence

Scatter Algorithm for Recommendation Systems: Methods and Evaluation

The article presents three scatter algorithms—column scatter, weight distribution, and sliding‑window—that reorder recommendation lists to disperse similar items, describing their mechanics, computational complexities, experimental trade‑offs, and a hybrid case study for efficient, multi‑dimensional list diversification.

Sliding Windowrankingrecommendation
0 likes · 10 min read
Scatter Algorithm for Recommendation Systems: Methods and Evaluation
DataFunTalk
DataFunTalk
Aug 5, 2020 · Artificial Intelligence

EdgeRec: An Edge‑Computing Based Real‑Time Recommendation System

The article introduces EdgeRec, an edge‑computing powered recommendation framework that moves user‑interest perception and ranking to the client side to overcome latency in traditional cloud‑centric recommender systems, detailing its architecture, heterogeneous behavior modeling, attention‑based reranking, and experimental gains.

Deep LearningEdge Computingranking
0 likes · 13 min read
EdgeRec: An Edge‑Computing Based Real‑Time Recommendation System
DataFunTalk
DataFunTalk
Jun 15, 2020 · Artificial Intelligence

Understanding and Handling Bad Cases in E-commerce Recommendation Systems

The article explores why bad cases occur in e‑commerce recommendation and search pipelines, classifies their types, demonstrates data‑driven analysis methods, and proposes practical online and offline strategies—including rule‑based fixes, model improvements, and iterative feedback loops—to continuously improve user experience and business metrics.

badcasedata analysise‑commerce
0 likes · 23 min read
Understanding and Handling Bad Cases in E-commerce Recommendation Systems
iQIYI Technical Product Team
iQIYI Technical Product Team
May 15, 2020 · Artificial Intelligence

iQIYI Deep Semantic Representation Learning Framework: Design, Challenges, and Applications

iQIYI’s deep semantic representation learning framework integrates multimodal content, knowledge graphs, and user behavior through layered data, feature, strategy, and application components, employing early, late, and hybrid fusion with Transformers, GCNs, and other deep models to deliver high‑quality embeddings that boost recommendation, search, and streaming performance across dozens of business scenarios.

Multimodalgraph neural networksiQIYI
0 likes · 28 min read
iQIYI Deep Semantic Representation Learning Framework: Design, Challenges, and Applications
21CTO
21CTO
Apr 24, 2020 · Artificial Intelligence

Why Your Recommendation System’s Offline Gains Fail Online: Common Pitfalls

This article examines the frequent pitfalls of recommendation systems—misleading metrics, over‑optimizing precision, data leakage, feature inconsistencies, and distribution bias—that cause offline AUC improvements to translate into lower online CTR and CPM, and offers practical mitigation strategies.

AIExploitationMetrics
0 likes · 15 min read
Why Your Recommendation System’s Offline Gains Fail Online: Common Pitfalls
JD Tech Talk
JD Tech Talk
Apr 8, 2020 · Artificial Intelligence

Designing and Evaluating Recommendation Algorithms for Wealth Management Platforms

This article explores how large wealth‑management platforms can model product recommendation as a mapping between customers and financial products, defines various evaluation goals such as transaction volume, revenue and user satisfaction, and outlines a systematic A/B‑testing workflow for comparing and optimizing recommendation algorithms.

A/B testingMetricsalgorithm
0 likes · 10 min read
Designing and Evaluating Recommendation Algorithms for Wealth Management Platforms
Huajiao Technology
Huajiao Technology
Apr 7, 2020 · Artificial Intelligence

How Huajiao Live Built a From‑Scratch Personalized Recommendation System

This article analyzes Huajiao Live's end‑to‑end recommendation pipeline, covering basic concepts, recall and ranking algorithms—including collaborative filtering, matrix factorization, deep learning models—and multi‑objective optimization, while detailing the engineering workflow for training, deployment, and real‑time serving in a live‑streaming environment.

AIDeep Learningcollaborative filtering
0 likes · 17 min read
How Huajiao Live Built a From‑Scratch Personalized Recommendation System
DataFunTalk
DataFunTalk
Mar 31, 2020 · Product Management

Building User Profiles: From Zero to One and Scaling to Hundreds

This article explains the concept of user profiling, outlines an eight‑dimensional tag architecture, describes step‑by‑step methods for constructing a robust profile system from scratch and expanding it, and shows how these profiles support statistical analysis, targeted marketing, and recommendation algorithms.

Data ProductMarketingTagging
0 likes · 18 min read
Building User Profiles: From Zero to One and Scaling to Hundreds
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 31, 2020 · Artificial Intelligence

How Alibaba’s AliCoCo Knowledge Graph Revolutionizes E‑Commerce Search & Recommendation

Alibaba’s AliCoCo, a large‑scale e‑commerce cognitive concept net, models user needs as graph nodes, linking concepts, primitives, taxonomy and items, and leverages advanced NLP, BiLSTM‑CRF, projection learning and knowledge‑enhanced models to boost search relevance, recommendation diversity, and overall user experience.

e‑commerceknowledge graphnatural language processing
0 likes · 25 min read
How Alibaba’s AliCoCo Knowledge Graph Revolutionizes E‑Commerce Search & Recommendation
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
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 27, 2020 · Artificial Intelligence

Multimodal Short Video Content Tagging Techniques and Applications at iQIYI

The article surveys iQIYI’s multimodal short‑video content‑tagging pipeline, detailing extraction‑ and generation‑based methods, challenges of open‑world tags, model evolution from rule‑based to Transformer generators, visual‑text fusion techniques, and applications such as recommendation, search, clustering, and future enhancements.

MultimodalNLPcontent tagging
0 likes · 18 min read
Multimodal Short Video Content Tagging Techniques and Applications at iQIYI
HomeTech
HomeTech
Mar 18, 2020 · Artificial Intelligence

Automobile Home Recommendation System Architecture and Ranking Models

This article presents a comprehensive overview of the Automobile Home recommendation system, detailing its objectives, architecture, various ranking models from LR to DeepFM, online learning mechanisms, service APIs, feature engineering pipelines, model training platforms, debugging tools, and future optimization directions.

AB testingAutoMLOnline Learning
0 likes · 18 min read
Automobile Home Recommendation System Architecture and Ranking Models
Architecture Digest
Architecture Digest
Mar 2, 2020 · Artificial Intelligence

Recommendation System Architecture and Practices at Toutiao

This article provides a comprehensive overview of Toutiao's recommendation system, covering its three-dimensional modeling of content, user, and environment features, various algorithmic approaches, feature extraction, real‑time training pipelines, recall strategies, user‑tag engineering, evaluation methods, and content‑safety measures.

A/B testingContent SafetyReal-time Training
0 likes · 18 min read
Recommendation System Architecture and Practices at Toutiao
DataFunTalk
DataFunTalk
Feb 28, 2020 · Artificial Intelligence

Evolution of Autohome's Recommendation System Ranking Algorithms

The article details the five‑year evolution of Autohome's recommendation system, covering its overall architecture, the progression of ranking models from LR to DeepFM and online learning, feature engineering pipelines, ranking service APIs, AB testing practices, and future optimization directions.

AB testingAIOnline Learning
0 likes · 20 min read
Evolution of Autohome's Recommendation System Ranking Algorithms
iQIYI Technical Product Team
iQIYI Technical Product Team
Feb 21, 2020 · Artificial Intelligence

Dual DNN Ranking Model with Online Knowledge Distillation for Recommender Systems

iQIYI’s dual‑DNN ranking model uses an online teacher‑student knowledge‑distillation framework where a complex teacher DNN shares representations with a lightweight student DNN, enabling end‑to‑end training, large‑scale feature crossing, and substantially higher recommendation accuracy while cutting inference latency and model size.

CTR predictionOnline Learningdual DNN
0 likes · 15 min read
Dual DNN Ranking Model with Online Knowledge Distillation for Recommender Systems
iQIYI Technical Product Team
iQIYI Technical Product Team
Feb 14, 2020 · Artificial Intelligence

Content Tagging Technology for Short Videos: Challenges and Multi‑Modal Model Evolution at iQIYI

iQIYI’s short‑video tagging system tackles multimodal fusion, open‑set and abstract tags by evolving from a text‑only model through cover‑image, BERT‑vector, and video‑frame fusion architectures, enabling automated labeling, personalized recommendation, and semantic search while planning to add OCR, audio, and knowledge‑graph enhancements.

BERTMultimodal LearningTransformer
0 likes · 13 min read
Content Tagging Technology for Short Videos: Challenges and Multi‑Modal Model Evolution at iQIYI
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
58 Tech
58 Tech
Dec 16, 2019 · Artificial Intelligence

Data Intelligence in the Used‑Car Business: User Traffic Prediction and Identification (Part 1)

This article details how the 58 Group applied data‑driven methods—user segmentation, interest description, clustering, and predictive modeling—to forecast and identify traffic in the used‑car scenario, illustrating the end‑to‑end pipeline, experimental results, and practical impact on downstream business processes.

Data IntelligenceTraffic PredictionUser Segmentation
0 likes · 19 min read
Data Intelligence in the Used‑Car Business: User Traffic Prediction and Identification (Part 1)
DataFunTalk
DataFunTalk
Dec 16, 2019 · Artificial Intelligence

A Comprehensive Overview of Sequential Recommendation Models and Techniques

This article provides an in-depth overview of sequential recommendation, defining the problem, discussing data preparation, and reviewing various neural architectures—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement‑learning approaches—while offering practical guidance on model selection and implementation.

CNNDeep LearningRNN
0 likes · 36 min read
A Comprehensive Overview of Sequential Recommendation Models and Techniques
DataFunTalk
DataFunTalk
Dec 4, 2019 · Artificial Intelligence

Joint Optimization of Tree‑based Index and Deep Model (JTM) for Large‑Scale Recommendation

This article presents JTM, a joint optimization framework that simultaneously learns a tree‑based index and a deep scoring model to overcome the limitations of traditional recommendation pipelines, demonstrating significant recall improvements on Amazon Books and Alibaba UserBehavior datasets through hierarchical user interest modeling and efficient tree learning.

Deep Learningjoint optimizationlarge scale
0 likes · 19 min read
Joint Optimization of Tree‑based Index and Deep Model (JTM) for Large‑Scale Recommendation
58 Tech
58 Tech
Nov 29, 2019 · Big Data

Application of Big Data and Algorithms in the Real‑Estate Internet

The talk presented at the Shanghai Computer Society Annual Meeting details how big data and algorithms are leveraged in the real‑estate internet sector to enhance user personalization, improve agent matching, and assess video quality, illustrating practical implementations and performance gains across data collection, modeling, and recommendation pipelines.

AIBig DataReal Estate
0 likes · 10 min read
Application of Big Data and Algorithms in the Real‑Estate Internet
21CTO
21CTO
Nov 27, 2019 · Big Data

How Xiaohongshu Scales Real‑Time Personalized Recommendations with Flink

The article summarizes Guo Yi’s 2019 Alibaba Cloud conference talk, outlining Xiaohongshu’s personalized recommendation architecture, detailing the data stack from ingestion to warehouse, and showcasing a Flink‑based real‑time multi‑dimensional user behavior aggregation use case, followed by a vision for the next year’s data architecture evolution.

Data ArchitectureFlinkReal-time Streaming
0 likes · 3 min read
How Xiaohongshu Scales Real‑Time Personalized Recommendations with Flink
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 27, 2019 · Artificial Intelligence

How Gaode Map Boosted Search Suggestions with Learning-to-Rank

Gaode Map revamped its search suggestion service by replacing rule‑based ranking with a Learning‑to‑Rank model, detailing challenges in sample construction, feature engineering, loss‑function tuning, and the resulting performance gains across millions of queries and diverse geographic regions.

Feature-EngineeringLBSLearning-to-Rank
0 likes · 11 min read
How Gaode Map Boosted Search Suggestions with Learning-to-Rank
FangDuoduo UEDC
FangDuoduo UEDC
Nov 19, 2019 · Mobile Development

Designing Effective Search Result Pages for Mobile Apps

A well‑crafted search result page—comprising a clear search box, organized results with categorisation, filtering and sorting, and smart recommendations—helps mobile users quickly locate desired content, reduces frustration during empty results, and boosts overall product satisfaction.

User experiencefilteringmobile app design
0 likes · 5 min read
Designing Effective Search Result Pages for Mobile Apps
Xianyu Technology
Xianyu Technology
Nov 13, 2019 · Artificial Intelligence

Real-time Feature Engineering for Recommendation Systems via Edge Computing

The article proposes moving real‑time feature engineering for recommendation systems from cloud to edge devices, enabling second‑level updates of user behavior features such as exposure, scroll speed, and clicks, which reduces latency, improves model freshness and recommendation accuracy through edge‑cloud collaboration.

Edge Computingmachine learningrecommendation
0 likes · 6 min read
Real-time Feature Engineering for Recommendation Systems via Edge Computing
Youzan Coder
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.

HBaseWide&Deepcold start
0 likes · 16 min read
Personalized Recommendation System Architecture and Techniques at Youzan
DataFunTalk
DataFunTalk
Oct 24, 2019 · Artificial Intelligence

Evolution and Engineering Practices of the 360 Display Advertising Recall System

This article details the 360 display advertising system's architecture and the progressive evolution of its recall module, covering business overview, overall pipeline, various recall strategies—including Boolean, vectorized, and deep‑tree approaches—and the performance optimizations applied to meet real‑time constraints.

AdvertisingDeep LearningVector Search
0 likes · 14 min read
Evolution and Engineering Practices of the 360 Display Advertising Recall System
58 Tech
58 Tech
Oct 12, 2019 · Artificial Intelligence

Recruitment Recommendation System: Ranking Framework, Model Evolution, and Feature Engineering

This article details 58.com’s recruitment recommendation platform, describing its personalized matching challenges, typical recommendation scenarios, a three‑stage ranking framework, optimization goals, the evolution from rule‑based methods to logistic regression, factorization machines, XGBoost, and deep learning models, extensive feature engineering practices, and future research directions.

AIDeep Learningfeature engineering
0 likes · 16 min read
Recruitment Recommendation System: Ranking Framework, Model Evolution, and Feature Engineering
Big Data Technology & Architecture
Big Data Technology & Architecture
Sep 23, 2019 · Backend Development

Design and Evolution of Feed Stream Architecture for High‑Throughput Applications

This article analyzes the business requirements, technical challenges, and mainstream architectural solutions for large‑scale feed streams, and proposes a step‑by‑step evolution path—from a simple push model using cloud Kafka and HBase to hybrid push‑pull and recommendation‑driven designs—suitable for startups and rapidly growing platforms.

BackendHBaseKafka
0 likes · 15 min read
Design and Evolution of Feed Stream Architecture for High‑Throughput Applications
DataFunTalk
DataFunTalk
Sep 23, 2019 · Artificial Intelligence

Understanding UC International Feed Recommendation: Goal Determination, Multi‑Objective Estimation, and Mixed Ranking

This article explains how UC international feed recommendation tackles goal definition, multi‑objective point estimation using models such as ESMM, DBMTL and MMoE, mixed‑ranking optimization, and cold‑start challenges by leveraging content understanding and feature generalization to improve user satisfaction.

AIcold-startmachine learning
0 likes · 12 min read
Understanding UC International Feed Recommendation: Goal Determination, Multi‑Objective Estimation, and Mixed Ranking
DataFunTalk
DataFunTalk
Sep 20, 2019 · Artificial Intelligence

Diversity as a Means, Not an End, in Recommendation Systems

The article argues that diversity should be treated as a tool rather than a final objective in recommendation systems, explains why it is hard to quantify, discusses appropriate metrics such as user feedback and engagement, and presents practical strategies—including expert rules, richer recall pipelines, and list‑wise modeling—to improve diversity while optimizing true business goals.

DiversityMetricslistwise
0 likes · 7 min read
Diversity as a Means, Not an End, in Recommendation Systems
Xianyu Technology
Xianyu Technology
Sep 18, 2019 · Artificial Intelligence

Edge‑Cloud Integrated Recommendation for Xianyu "Mario" Feature

By moving real‑time recommendation computation to users’ devices and using a two‑stage edge‑cloud CTR model to gate Xianyu’s Mario feature, the system cut server requests by 28%, boosted Mario’s click‑through rate by 31% and lifted overall conversion by 10% while preserving user privacy.

AICTREdge Computing
0 likes · 7 min read
Edge‑Cloud Integrated Recommendation for Xianyu "Mario" Feature
DataFunTalk
DataFunTalk
Sep 4, 2019 · Artificial Intelligence

Didi’s “Guess Where You’re Going” Feature: Product Benefits and Bayesian Gaussian Modeling

The article examines Didi’s “Guess Where You’re Going” feature, describing its product benefits, the contextual data used, and a simple Bayesian Gaussian model that predicts a user’s destination based on time, departure location, and weekday, while also discussing its limitations and potential improvements.

BayesianDidiGaussian
0 likes · 7 min read
Didi’s “Guess Where You’re Going” Feature: Product Benefits and Bayesian Gaussian Modeling
DataFunTalk
DataFunTalk
Sep 3, 2019 · Artificial Intelligence

Forward Neural Networks and Their Applications in Language Modeling, Ranking, and Recommendation

This article excerpt explains the structure and training of feed‑forward neural networks, illustrates their use in neural language models, describes deep structured semantic models for ranking tasks, and details two‑stage recommendation systems such as YouTube, covering both theoretical formulas and practical deployment considerations.

Artificial IntelligenceLanguage Modelforward neural network
0 likes · 13 min read
Forward Neural Networks and Their Applications in Language Modeling, Ranking, and Recommendation
DataFunTalk
DataFunTalk
Aug 30, 2019 · Artificial Intelligence

TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation

This article reviews the TransFM model, which combines the translation‑based sequential recommendation approach (TransRec) with factorization machines (FM), explains its formulation, optimization via sequential Bayesian personalized ranking, and demonstrates its superior performance on Amazon and Google Local datasets compared with several baselines.

Evaluationfactorization machinesmachine learning
0 likes · 8 min read
TransFM: Integrating Translation-based Recommendation and Factorization Machines for Sequential Recommendation