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Global AI-based Recommendation Engine Market Outlook, In‑Depth Analysis & Forecast to 2031
Published Date: November 2025
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Report Code: QYRE-Auto-1Z17013
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Global AI based Recommendation Engine Market Research Report 2024
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Global AI-based Recommendation Engine Market Outlook, In‑Depth Analysis & Forecast to 2031

Code: QYRE-Auto-1Z17013
Report
November 2025
Pages:116
QYResearch
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DESCRIPTION
TABLE OF CONTENT
TABLES & FIGURES
FEATURED COMPANIES
Microsoft
GOOGLE INC
BRAVE
Perplexity AI
NeevaAI
Qubit
Dynamic Yield
Andi Search
Metaphor AI
Phind

AI-based Recommendation Engine Market Size

The global AI-based Recommendation Engine market is projected to grow from US$ 2041 million in 2024 to US$ 3384 million by 2031, at a CAGR of 7.6% (2025-2031), driven by critical product segments and diverse end‑use applications.

AI-based Recommendation Engine Market

AI-based Recommendation Engine Market

AI-based recommendation system is a sophisticated tool that analyzes data to suggest relevant items to users. These systems are the driving force behind the "You might also like" sections across various digital platforms, whether it be in online shopping, streaming services, or social media. From a technical standpoint, these systems leverage machine learning algorithms to sift through large datasets. They identify patterns, preferences, and behaviors of users to predict what might interest them next. These algorithms can range from simple rule-based engines to complex neural networks that learn and evolve with each user interaction. They analyze past behavior, consider similar user profiles, and sometimes even incorporate external data to make their suggestions as relevant as possible.
The global AI-based recommendation system market refers to the use of artificial intelligence (AI) technologies to provide personalized recommendations to individuals based on their preferences, behaviors, and historical data. AI-based recommendation systems utilize algorithms and machine learning techniques to analyze large datasets and offer suggestions for products, services, content, or actions.
The market for AI-based recommendation systems is driven by several factors:
Growing demand for personalized experiences: With the increasing volume of digital content, products, and services available, consumers are seeking personalized experiences that cater to their specific needs and preferences. AI-based recommendation systems help businesses deliver tailored recommendations, enhancing customer engagement, satisfaction, and loyalty.
Rising e-commerce and online streaming activities: The proliferation of e-commerce platforms and online streaming services has generated vast amounts of data regarding consumer preferences and behavior. AI-based recommendation systems analyze this data to provide relevant product recommendations, improve cross-selling and upselling, and enhance the overall customer shopping or content consumption experience.
Advancements in AI and machine learning technologies: The advancements in AI and machine learning algorithms have significantly improved the capabilities of recommendation systems. Deep learning techniques, natural language processing, and collaborative filtering algorithms enable more accurate and effective personalized recommendations, driving the adoption of AI-based recommendation systems across various industries.
Focus on enhancing customer engagement and retention: Businesses are increasingly recognizing the importance of customer engagement and retention for long-term success. AI-based recommendation systems help in creating personalized customer experiences, increasing customer satisfaction, and encouraging repeat purchases or usage, thereby improving customer retention rates and revenue generation.
Integration of recommendation systems in various industries: AI-based recommendation systems are employed in diverse industries, including e-commerce, media and entertainment, healthcare, banking and finance, and travel and hospitality. These systems help in suggesting relevant products, content, treatments, financial services, or travel options, catering to the specific preferences and needs of individuals in each industry.
In conclusion, the global AI-based recommendation system market is witnessing significant growth due to the increased demand for personalized experiences, the rise in e-commerce and online streaming activities, advancements in AI and machine learning technologies, and the focus on customer engagement and retention. By leveraging AI algorithms and techniques, recommendation systems improve customer experiences, drive customer loyalty, and boost business revenue. With the continuous expansion of digital content and services, the AI-based recommendation system market is expected to grow further in the coming years.The global AI-based recommendation system market refers to the use of artificial intelligence (AI) technologies to provide personalized recommendations to individuals based on their preferences, behaviors, and historical data. AI-based recommendation systems utilize algorithms and machine learning techniques to analyze large datasets and offer suggestions for products, services, content, or actions.
The market for AI-based recommendation systems is driven by several factors:
Growing demand for personalized experiences: With the increasing volume of digital content, products, and services available, consumers are seeking personalized experiences that cater to their specific needs and preferences. AI-based recommendation systems help businesses deliver tailored recommendations, enhancing customer engagement, satisfaction, and loyalty.
Rising e-commerce and online streaming activities: The proliferation of e-commerce platforms and online streaming services has generated vast amounts of data regarding consumer preferences and behavior. AI-based recommendation systems analyze this data to provide relevant product recommendations, improve cross-selling and upselling, and enhance the overall customer shopping or content consumption experience.
Advancements in AI and machine learning technologies: The advancements in AI and machine learning algorithms have significantly improved the capabilities of recommendation systems. Deep learning techniques, natural language processing, and collaborative filtering algorithms enable more accurate and effective personalized recommendations, driving the adoption of AI-based recommendation systems across various industries.
Focus on enhancing customer engagement and retention: Businesses are increasingly recognizing the importance of customer engagement and retention for long-term success. AI-based recommendation systems help in creating personalized customer experiences, increasing customer satisfaction, and encouraging repeat purchases or usage, thereby improving customer retention rates and revenue generation.
Integration of recommendation systems in various industries: AI-based recommendation systems are employed in diverse industries, including e-commerce, media and entertainment, healthcare, banking and finance, and travel and hospitality. These systems help in suggesting relevant products, content, treatments, financial services, or travel options, catering to the specific preferences and needs of individuals in each industry.
In conclusion, the global AI-based recommendation system market is witnessing significant growth due to the increased demand for personalized experiences, the rise in e-commerce and online streaming activities, advancements in AI and machine learning technologies, and the focus on customer engagement and retention. By leveraging AI algorithms and techniques, recommendation systems improve customer experiences, drive customer loyalty, and boost business revenue. With the continuous expansion of digital content and services, the AI-based recommendation system market is expected to grow further in the coming years.

Report Includes:

This definitive report equips CEOs, marketing directors, and investors with a 360° view of the global AI-based Recommendation Engine market across value chain. It analyzes historical revenue data (2020–2024) and delivers forecasts through 2031, illuminating demand trends and growth drivers.
By segmenting the market by Type and by Application, the study quantifies market size, growth rates, niche opportunities, and substitution risks, and analyzes downstream customers distribution pattern.
Granular regional insights cover five major markets—North America, Europe, APAC, South America, and MEA—with in‑depth analysis of 20+ countries, detailing dominant products, competitive landscape, and downstream demand trends.
Critical competitive intelligence profiles players—revenue, margins, pricing strategies, and major customers—and dissects the top-player positioning across product lines, applications, and regions to reveal strategic strengths.
A concise Industry‑chain overview maps upstream, middlestream, and downstream distribution dynamics to identify strategic gaps and unmet demand.
Market Segmentation

Scope of AI-based Recommendation Engine Market Report

Report Metric Details
Report Name AI-based Recommendation Engine Market
Accounted market size in 2024 US$ 2041 in million
Forecasted market size in 2031 US$ 3384 million
CAGR 7.6%
Base Year 2024
Forecasted years 2025 - 2031
Segment by Type
  • Collaborative Filtering
  • Content Based Filtering
  • Hybrid Recommendation
Segment by Application
  • E-commerce Platform
  • Finance
  • Social Media
  • Others
Sales by Region
  • North America (United States, Canada)
  • Europe (Germany, France, UK, Italy, Russia) Rest of Europe
  • Nordic Countries
  • Asia-Pacific (China, Japan, South Korea)
  • Southeast Asia (India, Australia)
  • Rest of Asia
  • Latin America (Mexico, Brazil)
  • Rest of Latin America
  • Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of MEA)
By Company Microsoft, Google, Andi Search, Metaphor AI, Brave, Phind, Perplexity AI, NeevaAI, Qubit, Dynamic Yield
Forecast units USD million in value
Report coverage Revenue and volume forecast, company share, competitive landscape, growth factors and trends

Chapter Outline

  • Chapter 1: Defines the AI-based Recommendation Engine study scope, segments the market by Type and by Application, etc, highlights segment size and growth potential.
  • Chapter 2: Offers current market state, projects global revenue and sales to 2031, pinpointing high consumption regions and emerging market catalysts
  • Chapter 3: Dissects the player landscape—ranks by revenue and profitability, details Player performance by product type and evaluates concentration alongside M&A moves.
  • Chapter 4: Unlocks high margin product segments—compares revenue, ASP, and technology differentiators, highlighting growth niches and substitution risks
  • Chapter 5: Targets downstream market opportunities—evaluates market size by Application, identifies emerging use cases, and profiles leading customers by region and by Application.
  • Chapter 6: North America—breaks down market size by Type, by Application and country, profiles key players and assesses growth drivers and barriers.
  • Chapter 7: Europe—analyses regional market by Type, by Application and players, flagging drivers and barriers.
  • Chapter 8: Asia Pacific—quantifies market size by Type, by Application, and region/country, profiles top players, and uncovers high potential expansion areas.
  • Chapter 9: Central & South America—measures market size by Type, by Application, and country, profiles top players, and identifies investment opportunities and challenges.
  • Chapter 10: Middle East and Africa—evaluates market size by Type, by Application, and country, profiles key players, and outlines investment prospects and market hurdles
  • Chapter 11: Profiles players in depth—details product specs, revenue, margins; Top-tier players 2024 sales breakdowns by Product type, by Application, by region SWOT analysis, and recent strategic developments.
  • Chapter 12: Industry chain—analyses upstream, cost drivers, plus downstream channels.
  • Chapter 13: Market dynamics—explores drivers, restraints, regulatory impacts, and risk mitigation strategies.
  • Chapter 14: Actionable conclusions and strategic recommendations.
  • Why This Report:
  • Beyond standard market data, this analysis provides a clear profitability roadmap—empowering you to:
  • Allocate capital strategically to high growth regions (Chapters 6–10) and margin rich segments (Chapter 5).
  • Negotiate from strength with suppliers (Chapter 12) and customers (Chapter 5) using cost and demand intelligence.
  • Outmaneuver competitors with granular insights into their operations, margins, and strategies (Chapters 3 and 11).
  • Capitalize on the projected billion‑dollar opportunity with data‑driven regional and segment tactics (Chapter 12-14).
  • Leverage this 360° intelligence to turn market complexity into actionable competitive advantage.

FAQ for this report

How fast is AI-based Recommendation Engine Market growing?

Ans: The AI-based Recommendation Engine Market witnessing a CAGR of 7.6% during the forecast period 2025-2031.

What is the AI-based Recommendation Engine Market size in 2031?

Ans: The AI-based Recommendation Engine Market size in 2031 will be US$ 3384 million.

Who are the main players in the AI-based Recommendation Engine Market report?

Ans: The main players in the AI-based Recommendation Engine Market are Microsoft, Google, Andi Search, Metaphor AI, Brave, Phind, Perplexity AI, NeevaAI, Qubit, Dynamic Yield

What are the Application segmentation covered in the AI-based Recommendation Engine Market report?

Ans: The Applications covered in the AI-based Recommendation Engine Market report are E-commerce Platform, Finance, Social Media, Others

What are the Type segmentation covered in the AI-based Recommendation Engine Market report?

Ans: The Types covered in the AI-based Recommendation Engine Market report are Collaborative Filtering, Content Based Filtering, Hybrid Recommendation

1 Study Coverage
1.1 Introduction to AI-based Recommendation Engine: Definition, Properties, and Key Attributes
1.2 Market Segmentation by Type
1.2.1 Global AI-based Recommendation Engine Market Size by Type, 2020 VS 2024 VS 2031
1.2.2 Collaborative Filtering
1.2.3 Content Based Filtering
1.2.4 Hybrid Recommendation
1.3 Market Segmentation by Application
1.3.1 Global AI-based Recommendation Engine Market Size by Application, 2020 VS 2024 VS 2031
1.3.2 E-commerce Platform
1.3.3 Finance
1.3.4 Social Media
1.3.5 Others
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Executive Summary
2.1 Global AI-based Recommendation Engine Revenue Estimates and Forecasts 2020-2031
2.2 Global AI-based Recommendation Engine Revenue by Region
2.2.1 Revenue Comparison: 2020 VS 2024 VS 2031
2.2.2 Historical and Forecasted Revenue by Region (2020-2031)
2.2.3 Global Revenue Market Share by Region (2020-2031)
2.2.4 Emerging Market Focus: Growth Drivers & Investment Trends
3 Competition by Players
3.1 Global AI-based Recommendation Engine Player Revenue Rankings and Profitability
3.1.1 Global Revenue (Value) by Players (2020-2025)
3.1.2 Global Key Player Revenue Ranking (2023 vs. 2024)
3.1.3 Revenue-Based Tier Segmentation (Tier 1, Tier 2, and Tier 3)
3.1.4 Gross Margin by Top Player (2020 VS 2024)
3.2 Global AI-based Recommendation Engine Companies Headquarters and Service Footprint
3.3 Main Product Type Market Size by Players
3.3.1 Collaborative Filtering Market Size by Players
3.3.2 Content Based Filtering Market Size by Players
3.3.3 Hybrid Recommendation Market Size by Players
3.4 Global AI-based Recommendation Engine Market Concentration and Dynamics
3.4.1 Global Market Concentration (CR5 and HHI)
3.4.2 Entrant/Exit Impact Analysis
3.4.3 Strategic Moves: M&A, Expansion, R&D Investment
4 Global Product Segmentation Analysis
4.1 Global AI-based Recommendation Engine Revenue Trends by Type
4.1.1 Global Historical and Forecasted Revenue by Type (2020-2031)
4.1.2 Global Revenue Market Share by Type (2020-2031)
4.2 Key Product Attributes and Differentiation
4.3 Subtype Dynamics: Growth Leaders, Profitability and Risk
4.3.1 High-Growth Niches and Adoption Drivers
4.3.2 Profitability Hotspots and Cost Drivers
4.3.3 Substitution Threats
5 Global Downstream Application Analysis
5.1 Global AI-based Recommendation Engine Revenue by Application
5.1.1 Global Historical and Forecasted Revenue by Application (2020-2031)
5.1.2 Revenue Market Share by Application (2020-2031)
5.1.3 High-Growth Application Identification
5.1.4 Emerging Application Case Studies
5.2 Downstream Customer Analysis
5.2.1 Top Customers by Region
5.2.2 Top Customers by Application
6 North America
6.1 North America Market Size (2020-2031)
6.2 North America Key Players Revenue in 2024
6.3 North America AI-based Recommendation Engine Market Size by Type (2020-2031)
6.4 North America AI-based Recommendation Engine Market Size by Application (2020-2031)
6.5 North America Growth Accelerators and Market Barriers
6.6 North America AI-based Recommendation Engine Market Size by Country
6.6.1 North America Revenue Trends by Country
6.6.2 US
6.6.3 Canada
6.6.4 Mexico
7 Europe
7.1 Europe Market Size (2020-2031)
7.2 Europe Key Players Revenue in 2024
7.3 Europe AI-based Recommendation Engine Market Size by Type (2020-2031)
7.4 Europe AI-based Recommendation Engine Market Size by Application (2020-2031)
7.5 Europe Growth Accelerators and Market Barriers
7.6 Europe AI-based Recommendation Engine Market Size by Country
7.6.1 Europe Revenue Trends by Country
7.6.2 Germany
7.6.3 France
7.6.4 U.K.
7.6.5 Italy
7.6.6 Russia
8 Asia-Pacific
8.1 Asia-Pacific Market Size (2020-2031)
8.2 Asia-Pacific Key Players Revenue in 2024
8.3 Asia-Pacific AI-based Recommendation Engine Market Size by Type (2020-2031)
8.4 Asia-Pacific AI-based Recommendation Engine Market Size by Application (2020-2031)
8.5 Asia-Pacific Growth Accelerators and Market Barriers
8.6 Asia-Pacific AI-based Recommendation Engine Market Size by Region
8.6.1 Asia-Pacific Revenue Trends by Region
8.7 China
8.8 Japan
8.9 South Korea
8.10 Australia
8.11 India
8.12 Southeast Asia
8.12.1 Indonesia
8.12.2 Vietnam
8.12.3 Malaysia
8.12.4 Philippines
8.12.5 Singapore
9 Central and South America
9.1 Central and South America Market Size (2020-2031)
9.2 Central and South America Key Players Revenue in 2024
9.3 Central and South America AI-based Recommendation Engine Market Size by Type (2020-2031)
9.4 Central and South America AI-based Recommendation Engine Market Size by Application (2020-2031)
9.5 Central and South America Investment Opportunities and Key Challenges
9.6 Central and South America AI-based Recommendation Engine Market Size by Country
9.6.1 Central and South America Revenue Trends by Country (2020 VS 2024 VS 2031)
9.6.2 Brazil
9.6.3 Argentina
10 Middle East and Africa
10.1 Middle East and Africa Market Size (2020-2031)
10.2 Middle East and Africa Key Players Revenue in 2024
10.3 Middle East and Africa AI-based Recommendation Engine Market Size by Type (2020-2031)
10.4 Middle East and Africa AI-based Recommendation Engine Market Size by Application (2020-2031)
10.5 Middle East and Africa Investment Opportunities and Key Challenges
10.6 Middle East and Africa AI-based Recommendation Engine Market Size by Country
10.6.1 Middle East and Africa Revenue Trends by Country (2020 VS 2024 VS 2031)
10.6.2 GCC Countries
10.6.3 Israel
10.6.4 Egypt
10.6.5 South Africa
11 Corporate Profile
11.1 Microsoft
11.1.1 Microsoft Corporation Information
11.1.2 Microsoft Business Overview
11.1.3 Microsoft AI-based Recommendation Engine Product Features and Attributes
11.1.4 Microsoft AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.1.5 Microsoft AI-based Recommendation Engine Revenue by Product in 2024
11.1.6 Microsoft AI-based Recommendation Engine Revenue by Application in 2024
11.1.7 Microsoft AI-based Recommendation Engine Revenue by Geographic Area in 2024
11.1.8 Microsoft AI-based Recommendation Engine SWOT Analysis
11.1.9 Microsoft Recent Developments
11.2 Google
11.2.1 Google Corporation Information
11.2.2 Google Business Overview
11.2.3 Google AI-based Recommendation Engine Product Features and Attributes
11.2.4 Google AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.2.5 Google AI-based Recommendation Engine Revenue by Product in 2024
11.2.6 Google AI-based Recommendation Engine Revenue by Application in 2024
11.2.7 Google AI-based Recommendation Engine Revenue by Geographic Area in 2024
11.2.8 Google AI-based Recommendation Engine SWOT Analysis
11.2.9 Google Recent Developments
11.3 Andi Search
11.3.1 Andi Search Corporation Information
11.3.2 Andi Search Business Overview
11.3.3 Andi Search AI-based Recommendation Engine Product Features and Attributes
11.3.4 Andi Search AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.3.5 Andi Search AI-based Recommendation Engine Revenue by Product in 2024
11.3.6 Andi Search AI-based Recommendation Engine Revenue by Application in 2024
11.3.7 Andi Search AI-based Recommendation Engine Revenue by Geographic Area in 2024
11.3.8 Andi Search AI-based Recommendation Engine SWOT Analysis
11.3.9 Andi Search Recent Developments
11.4 Metaphor AI
11.4.1 Metaphor AI Corporation Information
11.4.2 Metaphor AI Business Overview
11.4.3 Metaphor AI AI-based Recommendation Engine Product Features and Attributes
11.4.4 Metaphor AI AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.4.5 Metaphor AI AI-based Recommendation Engine Revenue by Product in 2024
11.4.6 Metaphor AI AI-based Recommendation Engine Revenue by Application in 2024
11.4.7 Metaphor AI AI-based Recommendation Engine Revenue by Geographic Area in 2024
11.4.8 Metaphor AI AI-based Recommendation Engine SWOT Analysis
11.4.9 Metaphor AI Recent Developments
11.5 Brave
11.5.1 Brave Corporation Information
11.5.2 Brave Business Overview
11.5.3 Brave AI-based Recommendation Engine Product Features and Attributes
11.5.4 Brave AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.5.5 Brave AI-based Recommendation Engine Revenue by Product in 2024
11.5.6 Brave AI-based Recommendation Engine Revenue by Application in 2024
11.5.7 Brave AI-based Recommendation Engine Revenue by Geographic Area in 2024
11.5.8 Brave AI-based Recommendation Engine SWOT Analysis
11.5.9 Brave Recent Developments
11.6 Phind
11.6.1 Phind Corporation Information
11.6.2 Phind Business Overview
11.6.3 Phind AI-based Recommendation Engine Product Features and Attributes
11.6.4 Phind AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.6.5 Phind Recent Developments
11.7 Perplexity AI
11.7.1 Perplexity AI Corporation Information
11.7.2 Perplexity AI Business Overview
11.7.3 Perplexity AI AI-based Recommendation Engine Product Features and Attributes
11.7.4 Perplexity AI AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.7.5 Perplexity AI Recent Developments
11.8 NeevaAI
11.8.1 NeevaAI Corporation Information
11.8.2 NeevaAI Business Overview
11.8.3 NeevaAI AI-based Recommendation Engine Product Features and Attributes
11.8.4 NeevaAI AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.8.5 NeevaAI Recent Developments
11.9 Qubit
11.9.1 Qubit Corporation Information
11.9.2 Qubit Business Overview
11.9.3 Qubit AI-based Recommendation Engine Product Features and Attributes
11.9.4 Qubit AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.9.5 Qubit Recent Developments
11.10 Dynamic Yield
11.10.1 Dynamic Yield Corporation Information
11.10.2 Dynamic Yield Business Overview
11.10.3 Dynamic Yield AI-based Recommendation Engine Product Features and Attributes
11.10.4 Dynamic Yield AI-based Recommendation Engine Revenue and Gross Margin (2020-2025)
11.10.5 Company Ten Recent Developments
12 AI-based Recommendation EngineIndustry Chain Analysis
12.1 AI-based Recommendation Engine Industry Chain
12.2 Upstream Analysis
12.2.1 Upstream Key Suppliers
12.3 Middlestream Analysis
12.4 Downstream Sales Model and Distribution Networks
12.4.1 Sales Channels
12.4.2 Distributors
13 AI-based Recommendation Engine Market Dynamics
13.1 Industry Trends and Evolution
13.2 Market Growth Drivers and Emerging Opportunities
13.3 Market Challenges, Risks, and Restraints
14 Key Findings in the Global AI-based Recommendation Engine Study
15 Appendix
15.1 Research Methodology
15.1.1 Methodology/Research Approach
15.1.1.1 Research Programs/Design
15.1.1.2 Market Size Estimation
15.1.1.3 Market Breakdown and Data Triangulation
15.1.2 Data Source
15.1.2.1 Secondary Sources
15.1.2.2 Primary Sources
15.2 Author Details
FEATURED COMPANIES
Microsoft
GOOGLE INC
BRAVE
Perplexity AI
NeevaAI
Qubit
Dynamic Yield
Andi Search
Metaphor AI
Phind
List of Tables
 Table 1. Global AI-based Recommendation Engine Market Size Growth Rate by Type, 2020 VS 2024 VS 2031 (US$ Million)
 Table 2. Global AI-based Recommendation Engine Market Size Growth Rate by Application, 2020 VS 2024 VS 2031 (US$ Million)
 Table 3. Global AI-based Recommendation Engine Revenue Grow Rate (CAGR) by Region: 2020 VS 2024 VS 2031 (US$ Million)
 Table 4. Global AI-based Recommendation Engine Revenue by Region (2020-2025) & (US$ Million)
 Table 5. Global AI-based Recommendation Engine Revenue by Region (2026-2031) & (US$ Million)
 Table 6. Emerging Market Revenue Grow Rate (CAGR) by Country (2020 VS 2024 VS 2031) (US$ Million)
 Table 7. Global AI-based Recommendation Engine Revenue by Players (2020-2025) & (US$ Million)
 Table 8. Global AI-based Recommendation Engine Revenue Market Share by Players (2020-2025)
 Table 9. Global Key Players’Ranking Shift (2023 vs. 2024) (Based on Revenue)
 Table 10. Global AI-based Recommendation Engine by Player Tier (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in AI-based Recommendation Engine as of 2024)
 Table 11. Global AI-based Recommendation Engine Average Gross Margin (%) by Player (2020 VS 2024)
 Table 12. Global AI-based Recommendation Engine Companies Headquarters
 Table 13. Global AI-based Recommendation Engine Market Concentration Ratio (CR5 and HHI)
 Table 14. Key Market Entrant/Exit (2020-2024) – Drivers & Impact Analysis
 Table 15. Key Mergers & Acquisitions, Expansion Plans, R&D Investment
 Table 16. Global AI-based Recommendation Engine Revenue by Type (2020-2025) & (US$ Million)
 Table 17. Global AI-based Recommendation Engine Revenue by Type (2026-2031) & (US$ Million)
 Table 18. Key Product Attributes and Differentiation
 Table 19. Global AI-based Recommendation Engine Revenue by Application (2020-2025) & (US$ Million)
 Table 20. Global AI-based Recommendation Engine Revenue by Application (2026-2031) & (US$ Million)
 Table 21. AI-based Recommendation Engine High-Growth Sectors Demand CAGR (2024-2031)
 Table 22. Top Customers by Region
 Table 23. Top Customers by Application
 Table 24. North America AI-based Recommendation Engine Growth Accelerators and Market Barriers
 Table 25. North America AI-based Recommendation Engine Revenue Grow Rate (CAGR) by Country (2020 VS 2024 VS 2031) (US$ Million)
 Table 26. Europe AI-based Recommendation Engine Growth Accelerators and Market Barriers
 Table 27. Europe AI-based Recommendation Engine Revenue Grow Rate (CAGR) by Country: 2020 VS 2024 VS 2031 (US$ Million)
 Table 28. Asia-Pacific AI-based Recommendation Engine Growth Accelerators and Market Barriers
 Table 29. Asia-Pacific AI-based Recommendation Engine Revenue Grow Rate (CAGR) by Region: 2020 VS 2024 VS 2031 (US$ Million)
 Table 30. Central and South America AI-based Recommendation Engine Investment Opportunities and Key Challenges
 Table 31. Central and South America AI-based Recommendation Engine Revenue Grow Rate (CAGR) by Country (2020 VS 2024 VS 2031) (US$ Million)
 Table 32. Middle East and Africa AI-based Recommendation Engine Investment Opportunities and Key Challenges
 Table 33. Middle East and Africa AI-based Recommendation Engine Revenue Grow Rate (CAGR) by Country (2020 VS 2024 VS 2031) (US$ Million)
 Table 34. Microsoft Corporation Information
 Table 35. Microsoft Description and Major Businesses
 Table 36. Microsoft Product Features and Attributes
 Table 37. Microsoft Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 38. Microsoft Revenue Proportion by Product in 2024
 Table 39. Microsoft Revenue Proportion by Application in 2024
 Table 40. Microsoft Revenue Proportion by Geographic Area in 2024
 Table 41. Microsoft AI-based Recommendation Engine SWOT Analysis
 Table 42. Microsoft Recent Developments
 Table 43. Google Corporation Information
 Table 44. Google Description and Major Businesses
 Table 45. Google Product Features and Attributes
 Table 46. Google Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 47. Google Revenue Proportion by Product in 2024
 Table 48. Google Revenue Proportion by Application in 2024
 Table 49. Google Revenue Proportion by Geographic Area in 2024
 Table 50. Google AI-based Recommendation Engine SWOT Analysis
 Table 51. Google Recent Developments
 Table 52. Andi Search Corporation Information
 Table 53. Andi Search Description and Major Businesses
 Table 54. Andi Search Product Features and Attributes
 Table 55. Andi Search Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 56. Andi Search Revenue Proportion by Product in 2024
 Table 57. Andi Search Revenue Proportion by Application in 2024
 Table 58. Andi Search Revenue Proportion by Geographic Area in 2024
 Table 59. Andi Search AI-based Recommendation Engine SWOT Analysis
 Table 60. Andi Search Recent Developments
 Table 61. Metaphor AI Corporation Information
 Table 62. Metaphor AI Description and Major Businesses
 Table 63. Metaphor AI Product Features and Attributes
 Table 64. Metaphor AI Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 65. Metaphor AI Revenue Proportion by Product in 2024
 Table 66. Metaphor AI Revenue Proportion by Application in 2024
 Table 67. Metaphor AI Revenue Proportion by Geographic Area in 2024
 Table 68. Metaphor AI AI-based Recommendation Engine SWOT Analysis
 Table 69. Metaphor AI Recent Developments
 Table 70. Brave Corporation Information
 Table 71. Brave Description and Major Businesses
 Table 72. Brave Product Features and Attributes
 Table 73. Brave Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 74. Brave Revenue Proportion by Product in 2024
 Table 75. Brave Revenue Proportion by Application in 2024
 Table 76. Brave Revenue Proportion by Geographic Area in 2024
 Table 77. Brave AI-based Recommendation Engine SWOT Analysis
 Table 78. Brave Recent Developments
 Table 79. Phind Corporation Information
 Table 80. Phind Description and Major Businesses
 Table 81. Phind Product Features and Attributes
 Table 82. Phind Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 83. Phind Recent Developments
 Table 84. Perplexity AI Corporation Information
 Table 85. Perplexity AI Description and Major Businesses
 Table 86. Perplexity AI Product Features and Attributes
 Table 87. Perplexity AI Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 88. Perplexity AI Recent Developments
 Table 89. NeevaAI Corporation Information
 Table 90. NeevaAI Description and Major Businesses
 Table 91. NeevaAI Product Features and Attributes
 Table 92. NeevaAI Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 93. NeevaAI Recent Developments
 Table 94. Qubit Corporation Information
 Table 95. Qubit Description and Major Businesses
 Table 96. Qubit Product Features and Attributes
 Table 97. Qubit Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 98. Qubit Recent Developments
 Table 99. Dynamic Yield Corporation Information
 Table 100. Dynamic Yield Description and Major Businesses
 Table 101. Dynamic Yield Product Features and Attributes
 Table 102. Dynamic Yield Revenue (US$ Million) and Gross Margin (2020-2025)
 Table 103. Dynamic Yield Recent Developments
 Table 104. Raw Materials Key Suppliers
 Table 105. Distributors List
 Table 106. Market Trends and Market Evolution
 Table 107. Market Drivers and Opportunities
 Table 108. Market Challenges, Risks, and Restraints
 Table 109. Research Programs/Design for This Report
 Table 110. Key Data Information from Secondary Sources
 Table 111. Key Data Information from Primary Sources


List of Figures
 Figure 1. AI-based Recommendation Engine Product Picture
 Figure 2. Global AI-based Recommendation Engine Market Size Growth Rate by Type, 2020 VS 2024 VS 2031 (US$ Million)
 Figure 3. Collaborative Filtering Product Picture
 Figure 4. Content Based Filtering Product Picture
 Figure 5. Hybrid Recommendation Product Picture
 Figure 6. Global AI-based Recommendation Engine Market Size Growth Rate by Application, 2020 VS 2024 VS 2031 (US$ Million)
 Figure 7. E-commerce Platform
 Figure 8. Finance
 Figure 9. Social Media
 Figure 10. Others
 Figure 11. AI-based Recommendation Engine Report Years Considered
 Figure 12. Global AI-based Recommendation Engine Revenue, (US$ Million), 2020 VS 2024 VS 2031
 Figure 13. Global AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 14. Global AI-based Recommendation Engine Revenue (CAGR) by Region: 2020 VS 2024 VS 2031 (US$ Million)
 Figure 15. Global AI-based Recommendation Engine Revenue Market Share by Region (2020-2031)
 Figure 16. Global AI-based Recommendation Engine Revenue Market Share Ranking (2024)
 Figure 17. Tier Distribution by Revenue Contribution (2020 VS 2024)
 Figure 18. Collaborative Filtering Revenue Market Share by Player in 2024
 Figure 19. Content Based Filtering Revenue Market Share by Player in 2024
 Figure 20. Hybrid Recommendation Revenue Market Share by Player in 2024
 Figure 21. Global AI-based Recommendation Engine Revenue Market Share by Type (2020-2031)
 Figure 22. Global AI-based Recommendation Engine Revenue Market Share by Application (2020-2031)
 Figure 23. North America AI-based Recommendation Engine Revenue YoY (2020-2031) & (US$ Million)
 Figure 24. North America Top 5 Players AI-based Recommendation Engine Revenue (US$ Million) in 2024
 Figure 25. North America AI-based Recommendation Engine Revenue (US$ Million) by Type (2020 - 2031)
 Figure 26. North America AI-based Recommendation Engine Revenue (US$ Million) by Application (2020-2031)
 Figure 27. US AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 28. Canada AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 29. Mexico AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 30. Europe AI-based Recommendation Engine Revenue YoY (2020-2031) & (US$ Million)
 Figure 31. Europe Top 5 Players AI-based Recommendation Engine Revenue (US$ Million) in 2024
 Figure 32. Europe AI-based Recommendation Engine Revenue (US$ Million) by Type (2020-2031)
 Figure 33. Europe AI-based Recommendation Engine Revenue (US$ Million) by Application (2020-2031)
 Figure 34. Germany AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 35. France AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 36. U.K. AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 37. Italy AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 38. Russia AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 39. Asia-Pacific AI-based Recommendation Engine Revenue YoY (2020-2031) & (US$ Million)
 Figure 40. Asia-Pacific Top 8 Players AI-based Recommendation Engine Revenue (US$ Million) in 2024
 Figure 41. Asia-Pacific AI-based Recommendation Engine Revenue (US$ Million) by Type (2020-2031)
 Figure 42. Asia-Pacific AI-based Recommendation Engine Revenue (US$ Million) by Application (2020-2031)
 Figure 43. Indonesia AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 44. Japan AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 45. South Korea AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 46. Australia AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 47. India AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 48. Indonesia AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 49. Vietnam AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 50. Malaysia AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 51. Philippines AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 52. Singapore AI-based Recommendation Engine Revenue (2020-2031) & (US$ Million)
 Figure 53. Central and South America AI-based Recommendation Engine Revenue YoY (2020-2031) & (US$ Million)
 Figure 54. Central and South America Top 5 Players AI-based Recommendation Engine Revenue (US$ Million) in 2024
 Figure 55. Central and South America AI-based Recommendation Engine Revenue (US$ Million) by Type (2020-2031)
 Figure 56. Central and South America AI-based Recommendation Engine Revenue (US$ Million) by Application (2020-2031)
 Figure 57. Brazil AI-based Recommendation Engine Revenue (2020-2025) & (US$ Million)
 Figure 58. Argentina AI-based Recommendation Engine Revenue (2020-2025) & (US$ Million)
 Figure 59. Middle East and Africa AI-based Recommendation Engine Revenue YoY (2020-2031) & (US$ Million)
 Figure 60. Middle East and Africa Top 5 Players AI-based Recommendation Engine Revenue (US$ Million) in 2024
 Figure 61. South America AI-based Recommendation Engine Revenue (US$ Million) by Type (2020-2031)
 Figure 62. Middle East and Africa AI-based Recommendation Engine Revenue (US$ Million) by Application (2020-2031)
 Figure 63. GCC Countries AI-based Recommendation Engine Revenue (2020-2025) & (US$ Million)
 Figure 64. Israel AI-based Recommendation Engine Revenue (2020-2025) & (US$ Million)
 Figure 65. Egypt AI-based Recommendation Engine Revenue (2020-2025) & (US$ Million)
 Figure 66. South Africa AI-based Recommendation Engine Revenue (2020-2025) & (US$ Million)
 Figure 67. AI-based Recommendation Engine Industry Chain Mapping
 Figure 68. Channels of Distribution (Direct Vs Distribution)
 Figure 69. Bottom-up and Top-down Approaches for This Report
 Figure 70. Data Triangulation
 Figure 71. Key Executives Interviewed
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