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219 articles · Page 1 of 3
Kuaishou Tech
Kuaishou Tech
Jun 18, 2026 · Artificial Intelligence

Kuaishou Tech Team Highlights Multiple ICML 2026 Papers Across AI Domains

The Kuaishou technology team reports that several of its papers were accepted at the prestigious ICML 2026 conference—including a spotlight paper on metaphor video understanding, works on causal discovery for irregular time series, image super‑resolution, large‑scale notification dispatch, full‑order ranking, phase‑aware MoE for RL, end‑to‑end e‑commerce search, spatial‑reasoning rewards, a unified SWE benchmark, video temporal grounding, and interpretable transformers—while also inviting attendees to visit their booth B101 in Seoul.

Agentic AIICML 2026Kuaishou
0 likes · 18 min read
Kuaishou Tech Team Highlights Multiple ICML 2026 Papers Across AI Domains
Old Zhang's AI Learning
Old Zhang's AI Learning
May 16, 2026 · Artificial Intelligence

Inside X’s New For‑You Recommendation Pipeline: What Creators Must Know

The May 15 open‑source release of X’s For‑You recommendation system reveals a full pipeline—from query hydration and candidate sourcing to multi‑stage scoring—showing that the platform predicts a range of user actions, emphasizes content‑level signals, and offers creators concrete guidance to improve visibility.

GroxPhoenixRanking
0 likes · 17 min read
Inside X’s New For‑You Recommendation Pipeline: What Creators Must Know
Model Perspective
Model Perspective
May 15, 2026 · Industry Insights

XJTU Dominates 2026 MCM/ICM with 603 Points, Tsinghua Falls to 90th – Full Rankings Revealed

The 2026 MCM/ICM competition, the largest ever with over 32,000 teams and 94,000 participants, saw Xi'an Jiaotong University top the overall leaderboard with 603 points while Tsinghua University slipped to 90th, and the analysis breaks down award distributions, school rankings, per‑problem top performers, and notable trends across the six contest problems.

ICMMCMRanking
0 likes · 19 min read
XJTU Dominates 2026 MCM/ICM with 603 Points, Tsinghua Falls to 90th – Full Rankings Revealed
IoT Full-Stack Technology
IoT Full-Stack Technology
Apr 29, 2026 · Databases

10+ Practical Redis Use Cases You Can Implement Today

This article walks through more than ten common Redis scenarios—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmap statistics, shopping carts, timelines, message queues, lotteries, likes, product tagging, filtering, follow/fan relationships, and ranking—showing concrete command examples and code snippets for each.

CachingDistributed LockFollow System
0 likes · 9 min read
10+ Practical Redis Use Cases You Can Implement Today
java1234
java1234
Apr 5, 2026 · Databases

Beyond Caching: 16 Powerful Redis Use Cases

This article explores sixteen practical Redis applications—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, message queues, lotteries, likes, product tags, filtering, follow relationships, and ranking—demonstrating how Redis can serve as a versatile data store beyond simple caching.

BitmapsCachingData Structures
0 likes · 9 min read
Beyond Caching: 16 Powerful Redis Use Cases
DataFunTalk
DataFunTalk
Mar 30, 2026 · Artificial Intelligence

Building a Production-Ready RAG Engine for Office Knowledge Retrieval

This article examines the challenges of applying large language models in enterprise settings and presents a detailed, three‑layer RAG architecture—including offline ingestion, hybrid retrieval, multi‑stage ranking, and prompt‑engineered generation—along with practical insights, model choices, and deployment Q&A.

AIEnterprise Knowledge RetrievalHybrid Search
0 likes · 21 min read
Building a Production-Ready RAG Engine for Office Knowledge Retrieval
DataFunTalk
DataFunTalk
Mar 27, 2026 · Artificial Intelligence

Building a Production‑Ready RAG Engine: Architecture, Challenges & Solutions

This article examines the practical challenges of deploying Retrieval‑Augmented Generation in enterprise settings, outlines a layered RAG architecture with offline document processing and online query handling, and details the hybrid retrieval, multi‑stage ranking, knowledge filtering, and generation techniques that improve accuracy and reduce hallucinations.

AI EngineeringHybrid RetrievalKnowledge Filtering
0 likes · 22 min read
Building a Production‑Ready RAG Engine: Architecture, Challenges & Solutions
Sohu Tech Products
Sohu Tech Products
Jan 28, 2026 · Backend Development

How We Evolved a News App Comment System: From Threaded Views to AI‑Driven Ranking

This article details the evolution of a news‑app comment backend, covering early thread‑based displays, the transition to sharded databases and mixed adjacency‑path models, current hot‑comment ranking strategies, an in‑house experiment platform, topic aggregation via Kafka, and future AI‑driven architectural enhancements.

AIComment SystemDatabases
0 likes · 16 min read
How We Evolved a News App Comment System: From Threaded Views to AI‑Driven Ranking
Kuaishou Tech
Kuaishou Tech
Jan 19, 2026 · Artificial Intelligence

How OneSug Revolutionizes E‑commerce Query Suggestion with End‑to‑End Generative Modeling

OneSug introduces an end‑to‑end generative framework that unifies recall, coarse‑ranking, and fine‑ranking for e‑commerce query suggestion, addressing the limitations of traditional multi‑stage cascades and dramatically improving relevance, efficiency, and business metrics in real‑world deployments.

RankingRecommendation Systemse-commerce
0 likes · 10 min read
How OneSug Revolutionizes E‑commerce Query Suggestion with End‑to‑End Generative Modeling
Tencent Advertising Technology
Tencent Advertising Technology
Jan 8, 2026 · Artificial Intelligence

How Tencent Boosted Ad Experience by Up to 20% Using Reinforcement‑Learning‑Based Ranking

Tencent's ad tech team redesigned its ad ranking system by adding a parallel user‑experience‑optimized pipeline and evolving from manual CEM tuning to DDPG‑based reinforcement learning, achieving 10‑20% improvements in CTR, repeat‑view rates, and other experience metrics while maintaining overall spend.

AdvertisingRankingmulti-objective optimization
0 likes · 17 min read
How Tencent Boosted Ad Experience by Up to 20% Using Reinforcement‑Learning‑Based Ranking
ITPUB
ITPUB
Dec 29, 2025 · Databases

Boost PostgreSQL Full‑Text Search 3× Faster with VectorChord‑BM25

VectorChord‑BM25 is a PostgreSQL extension that adds native BM25 ranking and tokenization, delivering up to three‑fold query‑per‑second improvements over ElasticSearch while maintaining comparable relevance scores, and includes detailed installation, usage examples, and performance analysis.

BM25Database ExtensionFull-Text Search
0 likes · 17 min read
Boost PostgreSQL Full‑Text Search 3× Faster with VectorChord‑BM25
Model Perspective
Model Perspective
Dec 19, 2025 · Fundamentals

How a Multi‑Dimensional Model Ranks China’s Historical TV Dramas

This study builds a comprehensive evaluation model for Chinese historical drama series, defining four primary and nine secondary indicators, standardizing data, applying weighted calculations and a time‑compensation factor to score 127 candidates and produce a TOP‑100 ranking that highlights the influence of audience reputation, market impact, professional recognition, and historical value.

EvaluationModelRanking
0 likes · 18 min read
How a Multi‑Dimensional Model Ranks China’s Historical TV Dramas
Kuaishou Tech
Kuaishou Tech
Oct 30, 2025 · Artificial Intelligence

How EMER Revolutionizes Short‑Video Ranking with End‑to‑End Multi‑Objective Learning

This article details the EMER framework—a Transformer‑based, end‑to‑end multi‑objective ranking system that replaces handcrafted formulas with a learnable AI model, introduces relative‑satisfaction signals and dynamic loss weighting, and demonstrates significant offline and online performance gains in Kuaishou's short‑video recommendation pipeline.

AIRankingRecommendation Systems
0 likes · 16 min read
How EMER Revolutionizes Short‑Video Ranking with End‑to‑End Multi‑Objective Learning
Amap Tech
Amap Tech
Oct 17, 2025 · Artificial Intelligence

How Ranking Improves In-Context Example Retrieval: Insights from NeurIPS ’25

This article explains the limitations of current pointwise in‑context learning methods, introduces a novel ranking‑based approach called SeDPO that learns preference orders among examples, and demonstrates its superior performance across multiple NLP tasks through extensive experiments and ablation studies.

In-Context LearningNeurIPSRanking
0 likes · 10 min read
How Ranking Improves In-Context Example Retrieval: Insights from NeurIPS ’25
Lobster Programming
Lobster Programming
Sep 18, 2025 · Databases

Master MySQL Ranking: row_number, rank, and dense_rank Explained

Learn how MySQL window functions—row_number, rank, and dense_rank—can efficiently rank and rank‑tie data such as class scores or sales amounts, with clear syntax examples, differences in handling duplicate values, and practical SQL queries illustrated with real‑world scenarios.

ROW_NUMBERRankingSQL
0 likes · 6 min read
Master MySQL Ranking: row_number, rank, and dense_rank Explained
Kuaishou Tech
Kuaishou Tech
Jul 7, 2025 · Artificial Intelligence

8 Kuaishou Papers Spotlighted at ICML 2025: Multimodal AI, Causal Inference and More

Kuaishou has had eight cutting‑edge papers accepted at the International Conference on Machine Learning 2025, covering breakthroughs in multimodal emotion modeling, monotonic probability learning, causal effect generalization, cascade ranking, multimodal LLM alignment, ultra‑low‑rate image compression, and visual autoregressive super‑resolution, with links to each work and accompanying code repositories.

AIMultimodalRanking
0 likes · 13 min read
8 Kuaishou Papers Spotlighted at ICML 2025: Multimodal AI, Causal Inference and More
macrozheng
macrozheng
May 12, 2025 · Backend Development

Designing a Billion‑User Real‑Time Leaderboard: Redis vs MySQL

This article explores how to build a scalable, high‑performance leaderboard for hundreds of millions of users by comparing traditional database ORDER BY approaches with Redis sorted sets, addressing challenges such as hot keys, memory pressure, persistence risks, and presenting a divide‑and‑conquer implementation strategy.

High concurrencyLeaderboardRanking
0 likes · 11 min read
Designing a Billion‑User Real‑Time Leaderboard: Redis vs MySQL
Su San Talks Tech
Su San Talks Tech
May 7, 2025 · Backend Development

6 Scalable Leaderboard Solutions: From DB Sorting to Real‑Time Stream Processing

This article examines six different leaderboard implementation strategies—from simple database sorting and cache‑plus‑scheduled tasks to Redis sorted sets, sharded Redis clusters, pre‑computed layered caches, and real‑time stream processing with Flink—detailing their suitable scenarios, advantages, disadvantages, and architectural diagrams to help engineers choose the most appropriate solution.

CachingLeaderboardRanking
0 likes · 7 min read
6 Scalable Leaderboard Solutions: From DB Sorting to Real‑Time Stream Processing
JD Retail Technology
JD Retail Technology
Mar 25, 2025 · Artificial Intelligence

2024 Advances in Advertising Creative Generation and Selection

In 2024 the advertising team deployed an end‑to‑end AIGC pipeline that automatically creates high‑quality ad images, uses the multimodal Reliable Feedback Network and the million‑size RF1M dataset to filter outputs, builds rich offline and online multimodal representations with contrastive and list‑wise learning, and optimizes ranking architecture to deliver scalable, personalized creative selection.

AIAIGCAdvertising
0 likes · 10 min read
2024 Advances in Advertising Creative Generation and Selection
JD Tech
JD Tech
Feb 5, 2025 · Artificial Intelligence

Tech Insight: Highlights of Ten JD Retail Technology Papers Published in Top AI Conferences (2024)

Tech Insight presents concise overviews of ten JD retail technology papers accepted at top AI conferences in 2024, covering topics such as open‑vocabulary object detection, multi‑scenario ranking, diversity‑aware re‑ranking, a diversified product search dataset, semi‑supervised query classification, plug‑in CTR models, and methods to mitigate LLM hallucinations.

AIInformation RetrievalRanking
0 likes · 17 min read
Tech Insight: Highlights of Ten JD Retail Technology Papers Published in Top AI Conferences (2024)
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 18, 2024 · Artificial Intelligence

How STAR Enables Training‑Free Recommendations with Large Language Models

The article reviews the STAR framework, a training‑free recommendation approach that leverages large language model embeddings and collaborative co‑occurrence scores to retrieve and rank items, and evaluates its performance, hyper‑parameter effects, and ablation studies against existing LLM‑based recommender methods.

Information RetrievalLLMRanking
0 likes · 10 min read
How STAR Enables Training‑Free Recommendations with Large Language Models
JavaEdge
JavaEdge
Nov 21, 2024 · Backend Development

Inside Booking.com’s Real‑Time Ranking Engine: Architecture, Challenges & Solutions

Booking.com’s ranking platform uses sophisticated machine‑learning models and a multi‑cluster backend architecture to deliver personalized hotel search results, detailing data pipelines, feature engineering, service components, performance challenges, and optimization techniques such as static fallback, multi‑stage ranking, and model inference acceleration.

Ranking
0 likes · 13 min read
Inside Booking.com’s Real‑Time Ranking Engine: Architecture, Challenges & Solutions
JD Retail Technology
JD Retail Technology
Nov 6, 2024 · Artificial Intelligence

Explainability Practices in JD Retail Recommendation System

This article describes the definition, architecture, and practical applications of explainability in JD's retail recommendation system, covering ranking, model, and traffic explainability, system challenges, data infrastructure, and specific techniques such as SHAP and Integrated Gradients for interpreting model decisions.

AIRankingTraffic analysis
0 likes · 17 min read
Explainability Practices in JD Retail Recommendation System
DataFunSummit
DataFunSummit
Oct 21, 2024 · Artificial Intelligence

Retrieval‑Augmented Generation (RAG) for Office Applications: Architecture, Challenges, and Practical Practices

This article introduces Retrieval‑Augmented Generation (RAG) as a solution to the hallucination, freshness, and data‑privacy issues of large language models, details its modular architecture, explains the layered system design and hybrid retrieval pipeline, and shares the practical challenges and engineering tricks encountered when deploying RAG in enterprise office scenarios.

AIHybrid RetrievalLarge Language Model
0 likes · 19 min read
Retrieval‑Augmented Generation (RAG) for Office Applications: Architecture, Challenges, and Practical Practices
Architect
Architect
Oct 10, 2024 · Artificial Intelligence

Algorithmic Practices for Meituan's Content Intelligent Distribution

This article summarizes Meituan's content search system, detailing the challenges of heterogeneous, high‑frequency local content, the multi‑modal tagging and representation pipeline, recall and ranking optimizations, satisfaction metrics, multi‑objective fusion, heterogeneous mixing, and future directions for improving user experience in local life services.

AIMeituanRanking
0 likes · 18 min read
Algorithmic Practices for Meituan's Content Intelligent Distribution
Zhuanzhuan Tech
Zhuanzhuan Tech
Sep 4, 2024 · Backend Development

Optimization of Serialization in Search Recommendation Service

This report analyzes performance bottlenecks caused by serialization in a search‑recommendation system, presents detailed measurements of request latency, evaluates multiple optimization strategies—including Redis caching, lazy metric handling, and custom byte‑array serialization—and documents the resulting latency reductions and implementation considerations.

JavaRPCRanking
0 likes · 29 min read
Optimization of Serialization in Search Recommendation Service
JD Retail Technology
JD Retail Technology
Sep 4, 2024 · Artificial Intelligence

Multimodal Recommendation Algorithms and System Architecture at JD.com

This article presents JD.com’s multimodal recommendation system architecture, covering content understanding, multimodal ranking and recall models, practical deployment pipelines, and future research directions such as large‑model integration and supply‑side generation, all illustrated with detailed diagrams and Q&A.

AIJD.comMultimodal
0 likes · 14 min read
Multimodal Recommendation Algorithms and System Architecture at JD.com
Ximalaya Technology Team
Ximalaya Technology Team
Jul 12, 2024 · Artificial Intelligence

Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems

In real‑time bidding advertising, a multi‑path recall framework quickly filters billions of ads using parallel non‑personalized and personalized strategies—such as hot‑item rules, collaborative‑filtering, skip‑gram vectors, and GraphSAGE embeddings—while respecting targeting constraints, before a ranking stage optimizes eCPM, with effectiveness measured offline and online and future extensions planned with large language models.

AdvertisingGraph Neural NetworkRanking
0 likes · 18 min read
Multi-Path Recall and Ranking Techniques in Real-Time Bidding Advertising Systems
Code Ape Tech Column
Code Ape Tech Column
Jul 5, 2024 · Databases

10 Essential Intermediate to Advanced SQL Concepts

This article presents ten crucial intermediate‑to‑advanced SQL concepts—including CTEs, recursive queries, temporary functions, CASE pivots, EXCEPT vs NOT IN, self‑joins, ranking functions, delta calculations, cumulative totals, and date‑time manipulation—each explained with clear examples and code snippets.

CTEData PivotRanking
0 likes · 11 min read
10 Essential Intermediate to Advanced SQL Concepts
Programmer XiaoFu
Programmer XiaoFu
Jun 11, 2024 · Databases

10 Advanced MySQL Query Techniques Every Data Engineer Should Know

This article presents ten essential advanced MySQL concepts—including CTEs, recursive CTEs, temporary functions, CASE‑WHEN pivots, EXCEPT vs NOT IN, self‑joins, ranking window functions, delta calculations, running totals, and date‑time manipulation—each explained with clear examples and practical SQL snippets.

CASE WHENCTERanking
0 likes · 11 min read
10 Advanced MySQL Query Techniques Every Data Engineer Should Know
DaTaobao Tech
DaTaobao Tech
May 24, 2024 · Backend Development

Design and Optimization of High‑Concurrency Ranking and Real‑Time Messaging Systems

The article details a comprehensive architecture for high‑concurrency services—including a Redis‑backed ranking system, distributed locking, Bloom‑filter nickname deduplication, Netty‑based reliable messaging, IoT MQTT/REST integration, live‑streaming pipelines, layered performance tuning, and automated traffic‑replay testing to ensure scalability and robustness.

Distributed LockIoTRanking
0 likes · 38 min read
Design and Optimization of High‑Concurrency Ranking and Real‑Time Messaging Systems
Ximalaya Technology Team
Ximalaya Technology Team
Apr 30, 2024 · Artificial Intelligence

Multi‑Stage Funnel Architecture and Optimization Practices in an Advertising Engine

The advertising engine uses a five‑stage funnel—retrieval, recall, coarse ranking, fine ranking, and re‑ranking—each optimized with specialized indexes, multi‑channel recall, multi‑objective twin‑tower models, deep CTR/CVR predictors, and cold‑start paths, delivering up to 33 % spend growth, 6 % eCPM lift and lower latency while maintaining diversity.

AdvertisingRankingcold-start
0 likes · 15 min read
Multi‑Stage Funnel Architecture and Optimization Practices in an Advertising Engine
JD Tech Talk
JD Tech Talk
Apr 25, 2024 · Artificial Intelligence

Evolution of JD Recommendation Advertising Ranking and Auction Mechanisms

This article reviews the evolution of JD’s recommendation advertising ranking mechanism, covering its economic auction origins, challenges of multi‑material valuation, user interest uncertainty, and multi‑item auction fairness, and describes AI‑driven solutions such as deep auction models and reinforcement‑learning‑based ListVCG.

RankingRecommendation Systemsauction
0 likes · 19 min read
Evolution of JD Recommendation Advertising Ranking and Auction Mechanisms
JD Retail Technology
JD Retail Technology
Apr 15, 2024 · Artificial Intelligence

Design and Evolution of JD.com Recommendation Advertising Ranking Auction Mechanism

The article analyzes JD.com's recommendation advertising ranking auction mechanism, detailing its objectives, challenges in traffic value estimation, user interest exploration, and multi‑item auction fairness, and describing the technical evolution from traditional auctions to deep‑learning‑driven solutions.

AdvertisingRankingauction
0 likes · 18 min read
Design and Evolution of JD.com Recommendation Advertising Ranking Auction Mechanism
DataFunTalk
DataFunTalk
Apr 6, 2024 · Artificial Intelligence

Exploring Large Language Models for Recommendation Systems: Experiments and Insights

This article investigates how large language models can be applied to recommendation tasks, describing two usage strategies, various ranking approaches, experimental evaluations on multiple datasets, comparisons with traditional models, and analyses of prompt design, cost, and cold‑start capabilities.

LLMPrompt EngineeringRanking
0 likes · 13 min read
Exploring Large Language Models for Recommendation Systems: Experiments and Insights
Meituan Technology Team
Meituan Technology Team
Mar 21, 2024 · Artificial Intelligence

Algorithmic Practices for Meituan's Content Intelligent Distribution

This article outlines Meituan’s end‑to‑end content‑intelligent distribution pipeline, detailing challenges of massive multimodal search, supply labeling, semantic and personalized recall, multi‑objective ranking with distillation and MMoE/PPNet, heterogeneous mixing, and future plans for automated detection and large‑language‑model integration.

AIRankingcontent search
0 likes · 17 min read
Algorithmic Practices for Meituan's Content Intelligent Distribution
政采云技术
政采云技术
Dec 19, 2023 · Backend Development

Principles and Simple Implementation of a Search Engine in Go

This article explains the fundamental concepts of search engine technology—including forward and inverted indexes, tokenizers, stop words, synonym handling, ranking algorithms, and NLP integration—and provides a concise Go implementation with code examples and performance testing.

GoInformation RetrievalNLP
0 likes · 21 min read
Principles and Simple Implementation of a Search Engine in Go
HomeTech
HomeTech
Nov 8, 2023 · Artificial Intelligence

Cold Start Optimization for New Content in Autohome Recommendation System

The article details how Autohome tackled the cold‑start problem for newly generated content by redesigning the recommendation pipeline, introducing multi‑path recall, refining ranking and re‑ranking formulas, and applying strategic controls, resulting in a rise of cold‑start success rate from 27% to over 99% and a CTR increase from 5% to 14%.

AIRankingalgorithm optimization
0 likes · 10 min read
Cold Start Optimization for New Content in Autohome Recommendation System
Zhuanzhuan Tech
Zhuanzhuan Tech
Oct 18, 2023 · Artificial Intelligence

Design and Implementation of a Home‑Page Recommendation System Using Reinforcement Learning and DPP

This article presents a comprehensive design for Zhuanzhuan's home‑page recommendation pipeline, detailing the system architecture, challenges of traffic efficiency and diversity, and a two‑stage solution that applies Proximal Policy Optimization reinforcement learning in the re‑ranking module and Determinantal Point Process optimization in the coarse‑ranking and traffic‑pool stages, followed by offline simulation, online deployment, and evaluation metrics.

DPPRankingmachine learning
0 likes · 18 min read
Design and Implementation of a Home‑Page Recommendation System Using Reinforcement Learning and DPP
DataFunTalk
DataFunTalk
Sep 5, 2023 · Artificial Intelligence

Baidu Commercial Multimodal Understanding and AIGC Innovation Practices

This article presents Baidu's commercial multimodal understanding framework and AIGC innovations, detailing rich-media multimodal perception, the VICAN‑12B multimodal representation‑generation model, scenario‑specific fine‑tuning, feature quantization for ranking, and practical applications such as marketing content generation, digital‑human video creation, and poster synthesis.

AIGCBaiduMultimodal
0 likes · 12 min read
Baidu Commercial Multimodal Understanding and AIGC Innovation Practices
JD Cloud Developers
JD Cloud Developers
Aug 22, 2023 · Artificial Intelligence

A Practical Guide to Recommendation System Architecture and Methods

This article provides a concise overview of recommendation systems, covering their definition, core framework of recall, ranking, and re‑ranking, various recall strategies including multi‑path and vector‑based methods, similarity calculations, and practical implementation details such as AB testing and code examples.

AB testingInformation RetrievalRanking
0 likes · 14 min read
A Practical Guide to Recommendation System Architecture and Methods
JD Retail Technology
JD Retail Technology
Aug 18, 2023 · Artificial Intelligence

Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking

This article provides a comprehensive overview of recommendation systems, covering their definition, basic framework, request flow, AB testing, recall strategies (both non‑personalized and personalized), collaborative‑filtering methods, vector‑based retrieval, wide‑and‑deep models, and the MMR re‑ranking algorithm with code examples.

RankingVector Retrievalcollaborative filtering
0 likes · 14 min read
Overview of Recommendation Systems: Definitions, Architecture, Recall, Ranking, and Re‑ranking
DeWu Technology
DeWu Technology
Jul 24, 2023 · Artificial Intelligence

Design and Implementation of a Word Distribution Platform for Personalized Recommendations

The paper presents a unified word‑distribution platform that delivers personalized bottom‑words, hot‑words, and drop‑down suggestions across e‑commerce domains, detailing its preprocessing, recall, fusion, ranking, and re‑ranking pipelines, C++ engine migration, script hot‑deployment, visual configuration tools, and stability mechanisms for scalable, low‑maintenance guide services.

AIRankingSearch Engine
0 likes · 23 min read
Design and Implementation of a Word Distribution Platform for Personalized Recommendations
Meituan Technology Team
Meituan Technology Team
Jul 20, 2023 · Artificial Intelligence

Novelty Recommendation for Meituan Food Delivery: System Design, Challenges, and Solutions

Meituan’s food‑delivery team built a novelty‑focused recommendation pipeline—combining dual‑tower recall, novelty‑aware ranking, personalized mixed‑ranking weights, and reinforcement‑learning insertion—to surface merchants unseen by users, achieving 19% higher exposure novelty, 25% more order novelty, and improved ratings while keeping RPM loss under 0.5%.

Rankingfood deliverynovelty
0 likes · 28 min read
Novelty Recommendation for Meituan Food Delivery: System Design, Challenges, and Solutions
SQB Blog
SQB Blog
Jul 20, 2023 · Artificial Intelligence

How We Built and Optimized a Multi‑Pool Recommendation System for Boss Circle

This article explains the design, implementation, and iterative optimization of Boss Circle's recommendation engine, covering the initial simple ranking, the introduction of Elasticsearch‑based scoring, multi‑pool data sources, machine‑learning experiments, real‑time feature handling, and future personalization challenges.

ElasticsearchRankingdata pipelines
0 likes · 17 min read
How We Built and Optimized a Multi‑Pool Recommendation System for Boss Circle
dbaplus Community
dbaplus Community
Jul 19, 2023 · Artificial Intelligence

How Xianyu Built a Scalable Recommendation Platform for 10+ Scenarios

This article explains how Xianyu’s product recommendation system tackles massive data, diverse business scenarios, and engineering challenges by designing a unified recommendation middle‑platform that abstracts data, recall, ranking, and re‑ranking stages, enabling rapid scene onboarding and scalable model iteration.

AIPlatformRanking
0 likes · 14 min read
How Xianyu Built a Scalable Recommendation Platform for 10+ Scenarios
IT Services Circle
IT Services Circle
Jul 16, 2023 · Databases

Using MySQL Window Functions for Ranking, Aggregation, and Data Analysis

This article explains how MySQL 8.x window functions such as OVER, PARTITION BY, and ORDER BY can simplify complex ranking and aggregation queries, demonstrates creating a sample scores table, and provides practical examples of functions like ROW_NUMBER, RANK, DENSE_RANK, NTILE, LAG, and LEAD with their results.

AggregationOVERPartition
0 likes · 18 min read
Using MySQL Window Functions for Ranking, Aggregation, and Data Analysis
DataFunTalk
DataFunTalk
Jul 12, 2023 · Artificial Intelligence

Evolution of Search EE System: Adaptive Exploration, Scenario Modeling, End-to-End Scoring Consistency, and Context-Aware Brand Store Detection

This article outlines the recent full‑cycle iterations of JD’s search Explore‑Exploit (EE) system, covering adaptive dynamic detection models, upgraded scenario modeling, two‑stage scoring and insertion consistency, end‑to‑end dynamic insertion, and context‑aware brand‑store dimension detection, with detailed methodology, experiments, and online results.

Rankinge-commerceexplore‑exploit
0 likes · 22 min read
Evolution of Search EE System: Adaptive Exploration, Scenario Modeling, End-to-End Scoring Consistency, and Context-Aware Brand Store Detection
Alimama Tech
Alimama Tech
Jun 21, 2023 · Artificial Intelligence

Joint Optimization of Ranking and Calibration (JRC) for CTR Prediction

The Joint Optimization of Ranking and Calibration (JRC) model introduces a two‑logit generative‑discriminative architecture that jointly minimizes LogLoss for calibration and a listwise ranking loss, delivering superior GAUC and CTR performance across Alibaba’s display‑ad system, especially for sparse long‑tail users, while remaining simple to train and deploy.

CTR PredictionCalibrationHybrid Model
0 likes · 18 min read
Joint Optimization of Ranking and Calibration (JRC) for CTR Prediction
DeWu Technology
DeWu Technology
Jun 9, 2023 · Artificial Intelligence

Qianchuan Unified Recommendation Framework: Architecture, Challenges, and Algorithmic Solutions

Qianchuan is a unified recommendation platform that consolidates numerous low‑traffic, diverse scenarios into a five‑layer architecture—service, access, DPP, algorithm, and infrastructure—addressing challenges of varying products, goals, strategies, recommendation types, and limited resources through flexible product selection, multi‑goal support, advanced recall and ranking models, and extensible, low‑cost algorithms, while planning broader scene coverage, bias reduction, and componentized, reproducible solutions.

Rankingalgorithmmulti-scene
0 likes · 12 min read
Qianchuan Unified Recommendation Framework: Architecture, Challenges, and Algorithmic Solutions
DaTaobao Tech
DaTaobao Tech
Jun 9, 2023 · Artificial Intelligence

Generator-Evaluator Architecture for End-to-End Re-ranking in Information Flow

The paper introduces a Generator‑Evaluator (GE) architecture that end‑to‑end re‑ranks information‑flow items using a pointer‑network seq2seq generator and a reward‑estimating evaluator, jointly optimizing relevance and business utilities such as diversity, traffic control, inter‑group ordering, and fixed‑slot insertion, achieving over 70% better‑percentage and significant online gains on Taobao.

Information FlowRankinggenerator-evaluator
0 likes · 19 min read
Generator-Evaluator Architecture for End-to-End Re-ranking in Information Flow
DaTaobao Tech
DaTaobao Tech
Apr 24, 2023 · Artificial Intelligence

Daily Good Shop: Two‑Stage Card Ranking and Multi‑Task Modeling for E‑commerce Recommendations

Daily Good Shop improves e‑commerce recommendations by first ranking products with long‑term user behavior models, assembling top items into cards, then ranking those cards using a shared‑bottom multi‑task network that jointly predicts click, subscription and lead‑IPV, and finally re‑ranking card sequences via beam‑search, yielding over 2 % more clicks, 34 % more subscriptions, 33 % more lead‑IPV and 22 % longer dwell time.

Multi-Task LearningRankingrecommendation
0 likes · 11 min read
Daily Good Shop: Two‑Stage Card Ranking and Multi‑Task Modeling for E‑commerce Recommendations
HelloTech
HelloTech
Apr 3, 2023 · Artificial Intelligence

Integrating Machine Learning with Elasticsearch for Enhanced Ranking Capabilities

At the 2023 Elastic China Developer Conference in Shenzhen, Peng Cheng of Hello Technology will demonstrate how migrating online machine‑learning predictions into Elasticsearch can exploit its distributed architecture to rank thousands of models, expand model types and computational depth, and unlock new growth opportunities for business applications, underscoring the event’s status as China’s premier Elasticsearch open‑source technology forum.

ElasticsearchRankingTechnical Conference
0 likes · 2 min read
Integrating Machine Learning with Elasticsearch for Enhanced Ranking Capabilities
Baidu Geek Talk
Baidu Geek Talk
Mar 13, 2023 · Artificial Intelligence

Recent Advances in Sparse and Dense Retrieval for Search Engines

The article surveys recent academic advances in both sparse inverted‑index and dense semantic retrieval for large‑scale search, highlighting key papers on decision frameworks, benchmarks, sparse lexical models, dual encoders, and hybrid techniques, while discussing challenges such as single‑vector limits and proposing multi‑view and hybrid solutions.

Information RetrievalRankingdense retrieval
0 likes · 12 min read
Recent Advances in Sparse and Dense Retrieval for Search Engines
DataFunSummit
DataFunSummit
Feb 25, 2023 · Artificial Intelligence

Understanding Reward Model Training in InstructGPT Using Ranking Sequences

This article explains how InstructGPT's reward model is trained by collecting human‑annotated ranking sequences instead of absolute scores, describes the rank‑loss formulation, provides Python code for the model and loss computation, and presents experimental results demonstrating the approach.

InstructGPTPythonRLHF
0 likes · 9 min read
Understanding Reward Model Training in InstructGPT Using Ranking Sequences
DataFunTalk
DataFunTalk
Feb 17, 2023 · Artificial Intelligence

Full‑Chain Linkage Techniques for Alibaba Mama Display Advertising: From Precise Value Estimation to Set‑Selection Models

The article presents a comprehensive technical roadmap for Alibaba Mama's display advertising cascade ranking system, introducing full‑chain linkage, precise‑value estimation models (PDM, ESDM) and set‑selection approaches (LDM, LBDM), and demonstrates how these innovations jointly improve CTR and RPM while outlining future research directions.

AdvertisingRankingmachine learning
0 likes · 25 min read
Full‑Chain Linkage Techniques for Alibaba Mama Display Advertising: From Precise Value Estimation to Set‑Selection Models
DataFunSummit
DataFunSummit
Jan 25, 2023 · Artificial Intelligence

Expert Insights on Recommendation System Architecture, Data, Features, Recall, Ranking and Evaluation

This interview compiles expert opinions on the end‑to‑end recommendation system pipeline—including architecture, data collection, user profiling, content structuring, feature engineering, recall strategies, ranking algorithms, multi‑objective optimization, multi‑modal fusion, re‑ranking, cold‑start solutions, evaluation metrics and real‑world applications—highlighting the technical challenges and practical solutions.

Evaluation MetricsRankingcold-start
0 likes · 15 min read
Expert Insights on Recommendation System Architecture, Data, Features, Recall, Ranking and Evaluation
DataFunTalk
DataFunTalk
Jan 21, 2023 · Artificial Intelligence

Challenges and Best Practices in Recommendation Systems – Expert Interview

This interview with three recommendation‑system experts explores the technical architecture, data sources, feature engineering, recall and ranking strategies, evaluation metrics, cold‑start solutions, and practical difficulties, offering actionable insights to avoid common pitfalls in real‑world recommender deployments.

Evaluation MetricsMulti-modalRanking
0 likes · 15 min read
Challenges and Best Practices in Recommendation Systems – Expert Interview
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Jan 4, 2023 · Artificial Intelligence

Relevance Modeling and Ranking for Cloud Music Video Search

The paper details Cloud Music’s video‑search pipeline—query understanding, recall, relevance, ranking and re‑ranking—highlighting challenges such as ambiguous content, timeliness and multi‑objective goals, and describes two deployed models (a twin‑tower aspect relevance network and a click‑graph propagator) that together boost click‑through rate by 1.5 % and effective CTR by 2.3 %.

MultimodalRankingclick graph
0 likes · 24 min read
Relevance Modeling and Ranking for Cloud Music Video Search
政采云技术
政采云技术
Jan 4, 2023 · Artificial Intelligence

Overview of Recommendation and Search System Architecture: Recall and Ranking Techniques

This article explains the architecture of recommendation and search systems, detailing various recall methods such as collaborative filtering, matrix factorization, and vector‑based approaches, as well as ranking models like LR, FM, and DeepFM, and discusses re‑ranking and traffic control strategies.

Rankingartificial-intelligencerecall
0 likes · 14 min read
Overview of Recommendation and Search System Architecture: Recall and Ranking Techniques
21CTO
21CTO
Nov 9, 2022 · Fundamentals

Which Languages Are Rising? TIOBE November 2022 Rankings Unveiled

The November 2022 TIOBE index shows Rust re‑entering the Top 20 with a record 0.70% share, highlights shifts among PHP, SQL, Scratch and other languages, and provides detailed rankings from the top 10 to the 100th position, offering insight into programming language popularity trends.

Rankingrust
0 likes · 6 min read
Which Languages Are Rising? TIOBE November 2022 Rankings Unveiled
DataFunTalk
DataFunTalk
Nov 8, 2022 · Artificial Intelligence

Retrieval-Based Dialogue System Framework for Customer Service: Architecture, Retrieval, Ranking, and Practical Applications

This article presents a comprehensive retrieval‑based dialogue system designed to assist customer‑service agents by recommending candidate replies, detailing its five‑layer architecture, metric suite, text and vector retrieval modules, ranking strategies, and real‑world deployment results across multiple business scenarios.

AIRankingcustomer service
0 likes · 34 min read
Retrieval-Based Dialogue System Framework for Customer Service: Architecture, Retrieval, Ranking, and Practical Applications
Meituan Technology Team
Meituan Technology Team
Nov 3, 2022 · Artificial Intelligence

Retrieval‑Based Dialogue System for Customer Service at Meituan

This article details Meituan's retrieval‑based dialogue framework for customer service, covering its five‑layer architecture, offline‑to‑online metric system, text and vector recall strategies, ranking models with pre‑training and contrastive learning, and real‑world deployment results across multiple business scenarios.

AIMeituanRanking
0 likes · 38 min read
Retrieval‑Based Dialogue System for Customer Service at Meituan
MaGe Linux Operations
MaGe Linux Operations
Oct 20, 2022 · Databases

Boost Ranking Performance with Redis Sorted Sets and Go

This article explains why MySQL struggles with large‑scale ranking, introduces Redis sorted sets as a high‑performance alternative, and provides complete Go code examples—including direct command usage and a struct‑based wrapper—to implement, query, and manage ranked data efficiently.

GoRankingRedis
0 likes · 8 min read
Boost Ranking Performance with Redis Sorted Sets and Go
Model Perspective
Model Perspective
Oct 16, 2022 · Fundamentals

How the Keener Method Quantifies Team Strength Using Eigenvectors

The Keener method assigns numerical ratings to competing teams by linking each team's score to its absolute strength, which depends on relative strength against opponents, and uses linear‑algebraic techniques such as eigenvectors and the Perron‑Frobenius theorem to derive consistent rankings.

Perron-FrobeniusRankingeigenvector
0 likes · 13 min read
How the Keener Method Quantifies Team Strength Using Eigenvectors
Model Perspective
Model Perspective
Oct 13, 2022 · Artificial Intelligence

How BPR Transforms Recommendation Ranking: A Deep Dive

The article introduces the Bayesian Personalized Ranking (BPR) algorithm, explains its background in ranking‑based recommendation, details its probabilistic modeling assumptions, optimization objective, gradient‑based learning process, and compares it with matrix‑factorization methods like FunkSVD, providing a concise training workflow.

BPRRankingmatrix factorization
0 likes · 8 min read
How BPR Transforms Recommendation Ranking: A Deep Dive
DataFunTalk
DataFunTalk
Sep 10, 2022 · Artificial Intelligence

Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking

This article reviews how graph neural networks are applied across the three stages of recommendation systems—recall, ranking, and re‑ranking—detailing novel models such as NIA‑GCN, GraphSAIL, and DGENN, their experimental improvements, and future research directions.

GNN recallGraph Neural NetworksIncremental Learning
0 likes · 17 min read
Graph Neural Networks for Recommendation Systems: From Recall to Re‑ranking
IT Services Circle
IT Services Circle
Sep 5, 2022 · Databases

September 2023 DB-Engines Database Popularity Rankings Overview

The September 2023 DB-Engines ranking update shows Oracle’s steep decline, while MySQL and MongoDB gain points, and presents the top ten databases across relational, key‑value, document, time‑series, and graph categories along with the five metrics used to calculate popularity.

DB-EnginesMongoDBOracle
0 likes · 3 min read
September 2023 DB-Engines Database Popularity Rankings Overview
HelloTech
HelloTech
Sep 2, 2022 · Artificial Intelligence

Search and Recommendation Algorithms: Evolution, Common Pipelines, and Integrated Engine Design

The article outlines how search and recommendation systems have evolved from simple hot‑list displays to sophisticated, data‑driven pipelines comprising recall, fine‑ranking and re‑ranking stages, describes an integrated low‑code engine with standardized features, configurable components and intelligent modules that enable rapid deployment across many scenarios, delivering notable CTR, GMV and engagement gains at 哈啰.

Data StandardizationEmbeddingRanking
0 likes · 10 min read
Search and Recommendation Algorithms: Evolution, Common Pipelines, and Integrated Engine Design
macrozheng
macrozheng
Jul 23, 2022 · Databases

July 2023 DB-Engines Ranking: Oracle Drops, MySQL & SQL Server Rise

The July 2023 DB-Engines ranking shows Oracle, PostgreSQL and MongoDB slipping, while MySQL and Microsoft SQL Server climb, and provides detailed scores, trend charts, and the methodology behind the monthly popularity scores for the top ten database systems.

DB-EnginesDatabasesOracle
0 likes · 4 min read
July 2023 DB-Engines Ranking: Oracle Drops, MySQL & SQL Server Rise
Python Programming Learning Circle
Python Programming Learning Circle
Jul 20, 2022 · Databases

10 Essential SQL Concepts for Interview Preparation

This article presents ten core SQL techniques—including CTEs, recursive CTEs, temporary functions, CASE WHEN pivots, EXCEPT vs NOT IN, self‑joins, ranking window functions, delta calculations, cumulative sums, and date‑time manipulation—each explained with clear descriptions and practical query examples to help candidates ace data‑analysis interviews.

CASE WHENCTERanking
0 likes · 11 min read
10 Essential SQL Concepts for Interview Preparation
Dada Group Technology
Dada Group Technology
Jun 6, 2022 · Backend Development

Evolution of JD Daojia Search System Architecture from Version 1.0 to 3.0

The article details the progressive architectural evolution of JD Daojia's search system—starting from a simple, single‑layer ES‑based 1.0 design, through the 2.0 overhaul that introduced full‑recall, independent ranking services, and index disaster‑recovery, to the 3.0 version that adds multi‑path recall, sophisticated ranking models, and automated routing for high availability.

ElasticsearchRankingSearch
0 likes · 20 min read
Evolution of JD Daojia Search System Architecture from Version 1.0 to 3.0
HelloTech
HelloTech
May 30, 2022 · Artificial Intelligence

Harbor's Passive Growth Algorithms and Growth Engine: Practices and Insights

Harbor’s growth engine combines a passive, attribution‑driven traffic‑allocation algorithm with componentized ranking, search, and marketing systems—using pairwise/Listwise models, multi‑task CTR/CVR prediction, and automated strategy triggers—to align short‑term efficiency with long‑term LTV goals while moving toward causal inference and domain‑expert‑driven general models.

AIRankingRecommendation Systems
0 likes · 11 min read
Harbor's Passive Growth Algorithms and Growth Engine: Practices and Insights
Model Perspective
Model Perspective
May 15, 2022 · Fundamentals

Mastering TOPSIS: A Step‑by‑Step Guide to Multi‑Criteria Decision Making

TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) is a widely used multi‑criteria decision‑making method that ranks alternatives by their geometric distance to an ideal positive solution and farthest from a negative solution, with a straightforward seven‑step process from data normalization to final ranking.

RankingTOPSIS
0 likes · 4 min read
Mastering TOPSIS: A Step‑by‑Step Guide to Multi‑Criteria Decision Making
DataFunTalk
DataFunTalk
May 15, 2022 · Artificial Intelligence

Search Term Recommendation: Scenarios, Algorithm Design, Challenges and Future Directions

This article presents an in‑depth overview of search term recommendation in QQ Browser, covering the various recommendation scenarios, the composition of recommendation items, the multi‑stage algorithm architecture, key technical challenges, evaluation metrics, and future research directions such as multi‑task and session‑aware modeling.

Multi-Task LearningRankingfuture research
0 likes · 15 min read
Search Term Recommendation: Scenarios, Algorithm Design, Challenges and Future Directions
Architects' Tech Alliance
Architects' Tech Alliance
May 3, 2022 · Industry Insights

What Do China’s 2022 DB‑Engines Rankings Reveal About Database Trends?

The February 2022 DB‑Engines ranking shows Snowflake’s rapid rise, a sharp decline for Oracle, MySQL and Microsoft SQL Server, stable positions for Elasticsearch, and detailed score trends across relational, key‑value, document, time‑series and graph databases, highlighting five popularity metrics used for the assessment.

ChinaDB-EnginesDocument
0 likes · 4 min read
What Do China’s 2022 DB‑Engines Rankings Reveal About Database Trends?
DeWu Technology
DeWu Technology
Apr 18, 2022 · Artificial Intelligence

Warehouse Storage Location Recommendation: Architecture, Recall, and Ranking Strategies

The article outlines DeWu’s warehouse‑management recommendation system, which combines an online‑near‑line‑offline architecture to quickly recall viable shelf slots and rank them by space utilization, travel time, and sales potential, enabling automated, constraint‑aware placement that cuts picking time and inventory costs.

AIBig DataRanking
0 likes · 16 min read
Warehouse Storage Location Recommendation: Architecture, Recall, and Ranking Strategies
IT Services Circle
IT Services Circle
Apr 8, 2022 · Fundamentals

RedMonk 2022 Q1 Programming Language Rankings and Analysis

RedMonk's Q1 2022 programming language ranking, based on GitHub and Stack Overflow data, lists the top 20 languages, highlights the overall stability of the rankings, and provides detailed commentary on notable shifts for languages such as Python, PHP, C++, TypeScript, Dart, Kotlin, and Rust.

Language PopularityRankingRedMonk
0 likes · 6 min read
RedMonk 2022 Q1 Programming Language Rankings and Analysis
Tencent Cloud Developer
Tencent Cloud Developer
Mar 15, 2022 · Artificial Intelligence

Comprehensive Overview of Ranking Models in Recommendation Systems

The article provides a thorough guide to ranking in recommendation systems, detailing the pipeline architecture, sample handling challenges, extensive feature engineering categories, the evolution from collaborative filtering to deep and attention‑based models, and key optimization trade‑offs between memorization, generalization, and efficient user‑interest modeling.

CTR PredictionModel OptimizationRanking
0 likes · 19 min read
Comprehensive Overview of Ranking Models in Recommendation Systems
DataFunTalk
DataFunTalk
Mar 2, 2022 · Artificial Intelligence

Huya Live Streaming Recommendation Architecture: Business Background, System Design, Vector Retrieval, and Ranking

This article presents a comprehensive overview of Huya Live's recommendation system, covering business background, system architecture, vector retrieval techniques, ranking pipeline, technical challenges, implementation details, and future outlook, highlighting scalability and performance optimizations.

AIHuyaLive Streaming
0 likes · 14 min read
Huya Live Streaming Recommendation Architecture: Business Background, System Design, Vector Retrieval, and Ranking
58 Tech
58 Tech
Feb 24, 2022 · Artificial Intelligence

Deep Learning Ranking Model Enhancements for Recruitment Search at 58.com

This report details how the Search Recommendation team at 58.com upgraded their deep learning ranking model for recruitment by adding multi-valued and semantic vector features, integrating conversion sequences, employing feature‑crossing techniques, optimizing offline data pipelines, and planning future multi‑scene improvements to boost CTR and relevance.

AICTR PredictionRanking
0 likes · 18 min read
Deep Learning Ranking Model Enhancements for Recruitment Search at 58.com
Shopee Tech Team
Shopee Tech Team
Feb 17, 2022 · Artificial Intelligence

From Zero to One: Building and Optimizing Dropdown Recommendation in Shopee Chatbot

The article details Shopee Chatbot’s end‑to‑end development of a dropdown recommendation feature, describing the retrieve‑then‑rank architecture with BM25 and vector recalls, multilingual pre‑training and distillation, DeepFM‑based ranking, experimental gains in CTR and conversion, deployment infrastructure, business impact, and future enhancements.

CTR PredictionChatbotRanking
0 likes · 20 min read
From Zero to One: Building and Optimizing Dropdown Recommendation in Shopee Chatbot
DataFunTalk
DataFunTalk
Feb 5, 2022 · Artificial Intelligence

Evolution of 58 Local Service Recommendation Algorithms: Scenarios, Tag & Post Recommendations, and Future Directions

This article presents a comprehensive overview of 58 Local Service's recommendation system, detailing the diverse recommendation scenarios, challenges such as information homogeneity and complex user structures, the multi‑stage recall and ranking pipelines, model evolutions from statistical methods to deep learning, and future work to improve data quality and model efficiency.

ATRankCTRCVR
0 likes · 15 min read
Evolution of 58 Local Service Recommendation Algorithms: Scenarios, Tag & Post Recommendations, and Future Directions
58 Tech
58 Tech
Dec 16, 2021 · Artificial Intelligence

Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques

This talk presents the design and implementation of 58's commercial recruitment recommendation system, covering the business scenario, system architecture, regional and behavior‑based recall methods, various ranking models—including coarse‑ranking, dual‑tower, DIN‑bias, and multitask W3DA—and future optimization directions.

DBSCANEGESOnline Advertising
0 likes · 20 min read
Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques
DataFunTalk
DataFunTalk
Dec 12, 2021 · Artificial Intelligence

Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques

This talk presents the design and implementation of 58’s commercial recruitment recommendation system, covering the characteristics of the app’s recommendation scenario, system architecture, region‑based and behavior‑based recall methods, and coarse‑ and fine‑ranking models with various optimizations and future directions.

AIRankinge-commerce
0 likes · 20 min read
Commercial Recommendation System for 58 Recruitment: Architecture, Recall, and Ranking Techniques
DataFunSummit
DataFunSummit
Dec 12, 2021 · Artificial Intelligence

Design and Implementation of 58.com Commercial Recruitment Recommendation System

This article presents a comprehensive overview of the 58.com commercial recruitment recommendation system, detailing its business challenges, system architecture, region‑based and behavior‑based recall strategies, coarse‑ and fine‑ranking models, bias handling, evaluation methods, and future directions.

CTRDBSCANEGES
0 likes · 20 min read
Design and Implementation of 58.com Commercial Recruitment Recommendation System
Laravel Tech Community
Laravel Tech Community
Dec 9, 2021 · Fundamentals

TIOBE Programming Language Index – December 2021 Rankings and Trends

The December 2021 TIOBE index reveals the top 20 programming languages, highlights notable movements such as Python’s three‑month dominance, Swift’s rise into the top ten, and C# as a strong candidate for the upcoming annual award, while also explaining the index’s methodology and its limitations.

C#PythonRanking
0 likes · 5 min read
TIOBE Programming Language Index – December 2021 Rankings and Trends