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Global AI-based Recommendation Engine Market Research Report 2025
Synopsis
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 Engine market was valued at US$ 1910 million in 2023 and is anticipated to reach US$ 3167 million by 2030, witnessing a CAGR of 7.6% during the forecast period 2024-2030.
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.
This report aims to provide a comprehensive presentation of the global market for AI-based Recommendation Engine, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI-based Recommendation Engine.
The AI-based Recommendation Engine market size, estimations, and forecasts are provided in terms of and revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global AI-based Recommendation Engine market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the AI-based Recommendation Engine companies, new entrants, and industry chain related companies in this market with information on the revenues for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
Microsoft
Google
Andi Search
Metaphor AI
Brave
Phind
Perplexity AI
NeevaAI
Qubit
Dynamic Yield
Segment by Type
Collaborative Filtering
Content Based Filtering
Hybrid Recommendation
Segment by Application
E-commerce Platform
Finance
Social Media
Others
By Region
North America
United States
Canada
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Europe
Germany
France
U.K.
Italy
Russia
Nordic Countries
Rest of Europe
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of AI-based Recommendation Engine company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
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 Engine market was valued at US$ 1910 million in 2023 and is anticipated to reach US$ 3167 million by 2030, witnessing a CAGR of 7.6% during the forecast period 2024-2030.
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.
This report aims to provide a comprehensive presentation of the global market for AI-based Recommendation Engine, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI-based Recommendation Engine.
The AI-based Recommendation Engine market size, estimations, and forecasts are provided in terms of and revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global AI-based Recommendation Engine market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the AI-based Recommendation Engine companies, new entrants, and industry chain related companies in this market with information on the revenues for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
Microsoft
Andi Search
Metaphor AI
Brave
Phind
Perplexity AI
NeevaAI
Qubit
Dynamic Yield
Segment by Type
Collaborative Filtering
Content Based Filtering
Hybrid Recommendation
Segment by Application
E-commerce Platform
Finance
Social Media
Others
By Region
North America
United States
Canada
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Europe
Germany
France
U.K.
Italy
Russia
Nordic Countries
Rest of Europe
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of AI-based Recommendation Engine company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Index1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global AI-based Recommendation Engine Market Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Collaborative Filtering
1.2.3 Content Based Filtering
1.2.4 Hybrid Recommendation
1.3 Market by Application
1.3.1 Global AI-based Recommendation Engine Market Growth by Application: 2019 VS 2023 VS 2030
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 Global Growth Trends
2.1 Global AI-based Recommendation Engine Market Perspective (2019-2030)
2.2 Global AI-based Recommendation Engine Growth Trends by Region
2.2.1 Global AI-based Recommendation Engine Market Size by Region: 2019 VS 2023 VS 2030
2.2.2 AI-based Recommendation Engine Historic Market Size by Region (2019-2024)
2.2.3 AI-based Recommendation Engine Forecasted Market Size by Region (2025-2030)
2.3 AI-based Recommendation Engine Market Dynamics
2.3.1 AI-based Recommendation Engine Industry Trends
2.3.2 AI-based Recommendation Engine Market Drivers
2.3.3 AI-based Recommendation Engine Market Challenges
2.3.4 AI-based Recommendation Engine Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top AI-based Recommendation Engine Players by Revenue
3.1.1 Global Top AI-based Recommendation Engine Players by Revenue (2019-2024)
3.1.2 Global AI-based Recommendation Engine Revenue Market Share by Players (2019-2024)
3.2 Global AI-based Recommendation Engine Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Global Key Players Ranking by AI-based Recommendation Engine Revenue
3.4 Global AI-based Recommendation Engine Market Concentration Ratio
3.4.1 Global AI-based Recommendation Engine Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by AI-based Recommendation Engine Revenue in 2023
3.5 Global Key Players of AI-based Recommendation Engine Head office and Area Served
3.6 Global Key Players of AI-based Recommendation Engine, Product and Application
3.7 Global Key Players of AI-based Recommendation Engine, Date of Enter into This Industry
3.8 Mergers & Acquisitions, Expansion Plans
4 AI-based Recommendation Engine Breakdown Data by Type
4.1 Global AI-based Recommendation Engine Historic Market Size by Type (2019-2024)
4.2 Global AI-based Recommendation Engine Forecasted Market Size by Type (2025-2030)
5 AI-based Recommendation Engine Breakdown Data by Application
5.1 Global AI-based Recommendation Engine Historic Market Size by Application (2019-2024)
5.2 Global AI-based Recommendation Engine Forecasted Market Size by Application (2025-2030)
6 North America
6.1 North America AI-based Recommendation Engine Market Size (2019-2030)
6.2 North America AI-based Recommendation Engine Market Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America AI-based Recommendation Engine Market Size by Country (2019-2024)
6.4 North America AI-based Recommendation Engine Market Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe AI-based Recommendation Engine Market Size (2019-2030)
7.2 Europe AI-based Recommendation Engine Market Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe AI-based Recommendation Engine Market Size by Country (2019-2024)
7.4 Europe AI-based Recommendation Engine Market Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific AI-based Recommendation Engine Market Size (2019-2030)
8.2 Asia-Pacific AI-based Recommendation Engine Market Growth Rate by Country: 2019 VS 2023 VS 2030
8.3 Asia-Pacific AI-based Recommendation Engine Market Size by Region (2019-2024)
8.4 Asia-Pacific AI-based Recommendation Engine Market Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America AI-based Recommendation Engine Market Size (2019-2030)
9.2 Latin America AI-based Recommendation Engine Market Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America AI-based Recommendation Engine Market Size by Country (2019-2024)
9.4 Latin America AI-based Recommendation Engine Market Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa AI-based Recommendation Engine Market Size (2019-2030)
10.2 Middle East & Africa AI-based Recommendation Engine Market Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa AI-based Recommendation Engine Market Size by Country (2019-2024)
10.4 Middle East & Africa AI-based Recommendation Engine Market Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Microsoft
11.1.1 Microsoft Company Details
11.1.2 Microsoft Business Overview
11.1.3 Microsoft AI-based Recommendation Engine Introduction
11.1.4 Microsoft Revenue in AI-based Recommendation Engine Business (2019-2024)
11.1.5 Microsoft Recent Development
11.2 Google
11.2.1 Google Company Details
11.2.2 Google Business Overview
11.2.3 Google AI-based Recommendation Engine Introduction
11.2.4 Google Revenue in AI-based Recommendation Engine Business (2019-2024)
11.2.5 Google Recent Development
11.3 Andi Search
11.3.1 Andi Search Company Details
11.3.2 Andi Search Business Overview
11.3.3 Andi Search AI-based Recommendation Engine Introduction
11.3.4 Andi Search Revenue in AI-based Recommendation Engine Business (2019-2024)
11.3.5 Andi Search Recent Development
11.4 Metaphor AI
11.4.1 Metaphor AI Company Details
11.4.2 Metaphor AI Business Overview
11.4.3 Metaphor AI AI-based Recommendation Engine Introduction
11.4.4 Metaphor AI Revenue in AI-based Recommendation Engine Business (2019-2024)
11.4.5 Metaphor AI Recent Development
11.5 Brave
11.5.1 Brave Company Details
11.5.2 Brave Business Overview
11.5.3 Brave AI-based Recommendation Engine Introduction
11.5.4 Brave Revenue in AI-based Recommendation Engine Business (2019-2024)
11.5.5 Brave Recent Development
11.6 Phind
11.6.1 Phind Company Details
11.6.2 Phind Business Overview
11.6.3 Phind AI-based Recommendation Engine Introduction
11.6.4 Phind Revenue in AI-based Recommendation Engine Business (2019-2024)
11.6.5 Phind Recent Development
11.7 Perplexity AI
11.7.1 Perplexity AI Company Details
11.7.2 Perplexity AI Business Overview
11.7.3 Perplexity AI AI-based Recommendation Engine Introduction
11.7.4 Perplexity AI Revenue in AI-based Recommendation Engine Business (2019-2024)
11.7.5 Perplexity AI Recent Development
11.8 NeevaAI
11.8.1 NeevaAI Company Details
11.8.2 NeevaAI Business Overview
11.8.3 NeevaAI AI-based Recommendation Engine Introduction
11.8.4 NeevaAI Revenue in AI-based Recommendation Engine Business (2019-2024)
11.8.5 NeevaAI Recent Development
11.9 Qubit
11.9.1 Qubit Company Details
11.9.2 Qubit Business Overview
11.9.3 Qubit AI-based Recommendation Engine Introduction
11.9.4 Qubit Revenue in AI-based Recommendation Engine Business (2019-2024)
11.9.5 Qubit Recent Development
11.10 Dynamic Yield
11.10.1 Dynamic Yield Company Details
11.10.2 Dynamic Yield Business Overview
11.10.3 Dynamic Yield AI-based Recommendation Engine Introduction
11.10.4 Dynamic Yield Revenue in AI-based Recommendation Engine Business (2019-2024)
11.10.5 Dynamic Yield Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.1.1 Research Programs/Design
13.1.1.2 Market Size Estimation
13.1.1.3 Market Breakdown and Data Triangulation
13.1.2 Data Source
13.1.2.1 Secondary Sources
13.1.2.2 Primary Sources
13.2 Author Details
13.3 Disclaimer
List of TablesList of Tables
Table 1. Global AI-based Recommendation Engine Market Size Growth Rate by Type (US$ Million): 2019 VS 2023 VS 2030
Table 2. Key Players of Collaborative Filtering
Table 3. Key Players of Content Based Filtering
Table 4. Key Players of Hybrid Recommendation
Table 5. Global AI-based Recommendation Engine Market Size Growth by Application (US$ Million): 2019 VS 2023 VS 2030
Table 6. Global AI-based Recommendation Engine Market Size by Region (US$ Million): 2019 VS 2023 VS 2030
Table 7. Global AI-based Recommendation Engine Market Size by Region (2019-2024) & (US$ Million)
Table 8. Global AI-based Recommendation Engine Market Share by Region (2019-2024)
Table 9. Global AI-based Recommendation Engine Forecasted Market Size by Region (2025-2030) & (US$ Million)
Table 10. Global AI-based Recommendation Engine Market Share by Region (2025-2030)
Table 11. AI-based Recommendation Engine Market Trends
Table 12. AI-based Recommendation Engine Market Drivers
Table 13. AI-based Recommendation Engine Market Challenges
Table 14. AI-based Recommendation Engine Market Restraints
Table 15. Global AI-based Recommendation Engine Revenue by Players (2019-2024) & (US$ Million)
Table 16. Global AI-based Recommendation Engine Market Share by Players (2019-2024)
Table 17. Global Top AI-based Recommendation Engine Players by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in AI-based Recommendation Engine as of 2023)
Table 18. Ranking of Global Top AI-based Recommendation Engine Companies by Revenue (US$ Million) in 2023
Table 19. Global 5 Largest Players Market Share by AI-based Recommendation Engine Revenue (CR5 and HHI) & (2019-2024)
Table 20. Global Key Players of AI-based Recommendation Engine, Headquarters and Area Served
Table 21. Global Key Players of AI-based Recommendation Engine, Product and Application
Table 22. Global Key Players of AI-based Recommendation Engine, Date of Enter into This Industry
Table 23. Mergers & Acquisitions, Expansion Plans
Table 24. Global AI-based Recommendation Engine Market Size by Type (2019-2024) & (US$ Million)
Table 25. Global AI-based Recommendation Engine Revenue Market Share by Type (2019-2024)
Table 26. Global AI-based Recommendation Engine Forecasted Market Size by Type (2025-2030) & (US$ Million)
Table 27. Global AI-based Recommendation Engine Revenue Market Share by Type (2025-2030)
Table 28. Global AI-based Recommendation Engine Market Size by Application (2019-2024) & (US$ Million)
Table 29. Global AI-based Recommendation Engine Revenue Market Share by Application (2019-2024)
Table 30. Global AI-based Recommendation Engine Forecasted Market Size by Application (2025-2030) & (US$ Million)
Table 31. Global AI-based Recommendation Engine Revenue Market Share by Application (2025-2030)
Table 32. North America AI-based Recommendation Engine Market Size Growth Rate by Country (US$ Million): 2019 VS 2023 VS 2030
Table 33. North America AI-based Recommendation Engine Market Size by Country (2019-2024) & (US$ Million)
Table 34. North America AI-based Recommendation Engine Market Size by Country (2025-2030) & (US$ Million)
Table 35. Europe AI-based Recommendation Engine Market Size Growth Rate by Country (US$ Million): 2019 VS 2023 VS 2030
Table 36. Europe AI-based Recommendation Engine Market Size by Country (2019-2024) & (US$ Million)
Table 37. Europe AI-based Recommendation Engine Market Size by Country (2025-2030) & (US$ Million)
Table 38. Asia-Pacific AI-based Recommendation Engine Market Size Growth Rate by Country (US$ Million): 2019 VS 2023 VS 2030
Table 39. Asia-Pacific AI-based Recommendation Engine Market Size by Region (2019-2024) & (US$ Million)
Table 40. Asia-Pacific AI-based Recommendation Engine Market Size by Region (2025-2030) & (US$ Million)
Table 41. Latin America AI-based Recommendation Engine Market Size Growth Rate by Country (US$ Million): 2019 VS 2023 VS 2030
Table 42. Latin America AI-based Recommendation Engine Market Size by Country (2019-2024) & (US$ Million)
Table 43. Latin America AI-based Recommendation Engine Market Size by Country (2025-2030) & (US$ Million)
Table 44. Middle East & Africa AI-based Recommendation Engine Market Size Growth Rate by Country (US$ Million): 2019 VS 2023 VS 2030
Table 45. Middle East & Africa AI-based Recommendation Engine Market Size by Country (2019-2024) & (US$ Million)
Table 46. Middle East & Africa AI-based Recommendation Engine Market Size by Country (2025-2030) & (US$ Million)
Table 47. Microsoft Company Details
Table 48. Microsoft Business Overview
Table 49. Microsoft AI-based Recommendation Engine Product
Table 50. Microsoft Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 51. Microsoft Recent Development
Table 52. Google Company Details
Table 53. Google Business Overview
Table 54. Google AI-based Recommendation Engine Product
Table 55. Google Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 56. Google Recent Development
Table 57. Andi Search Company Details
Table 58. Andi Search Business Overview
Table 59. Andi Search AI-based Recommendation Engine Product
Table 60. Andi Search Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 61. Andi Search Recent Development
Table 62. Metaphor AI Company Details
Table 63. Metaphor AI Business Overview
Table 64. Metaphor AI AI-based Recommendation Engine Product
Table 65. Metaphor AI Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 66. Metaphor AI Recent Development
Table 67. Brave Company Details
Table 68. Brave Business Overview
Table 69. Brave AI-based Recommendation Engine Product
Table 70. Brave Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 71. Brave Recent Development
Table 72. Phind Company Details
Table 73. Phind Business Overview
Table 74. Phind AI-based Recommendation Engine Product
Table 75. Phind Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 76. Phind Recent Development
Table 77. Perplexity AI Company Details
Table 78. Perplexity AI Business Overview
Table 79. Perplexity AI AI-based Recommendation Engine Product
Table 80. Perplexity AI Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 81. Perplexity AI Recent Development
Table 82. NeevaAI Company Details
Table 83. NeevaAI Business Overview
Table 84. NeevaAI AI-based Recommendation Engine Product
Table 85. NeevaAI Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 86. NeevaAI Recent Development
Table 87. Qubit Company Details
Table 88. Qubit Business Overview
Table 89. Qubit AI-based Recommendation Engine Product
Table 90. Qubit Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 91. Qubit Recent Development
Table 92. Dynamic Yield Company Details
Table 93. Dynamic Yield Business Overview
Table 94. Dynamic Yield AI-based Recommendation Engine Product
Table 95. Dynamic Yield Revenue in AI-based Recommendation Engine Business (2019-2024) & (US$ Million)
Table 96. Dynamic Yield Recent Development
Table 97. Research Programs/Design for This Report
Table 98. Key Data Information from Secondary Sources
Table 99. Key Data Information from Primary Sources
Table 100. Authors List of This Report
List of Figures
Figure 1. AI-based Recommendation Engine Picture
Figure 2. Global AI-based Recommendation Engine Market Size Comparison by Type (2024-2030) & (US$ Million)
Figure 3. Global AI-based Recommendation Engine Market Share by Type: 2023 VS 2030
Figure 4. Collaborative Filtering Features
Figure 5. Content Based Filtering Features
Figure 6. Hybrid Recommendation Features
Figure 7. Global AI-based Recommendation Engine Market Size by Application (2024-2030) & (US$ Million)
Figure 8. Global AI-based Recommendation Engine Market Share by Application: 2023 VS 2030
Figure 9. E-commerce Platform Case Studies
Figure 10. Finance Case Studies
Figure 11. Social Media Case Studies
Figure 12. Others Case Studies
Figure 13. AI-based Recommendation Engine Report Years Considered
Figure 14. Global AI-based Recommendation Engine Market Size (US$ Million), Year-over-Year: 2019-2030
Figure 15. Global AI-based Recommendation Engine Market Size, (US$ Million), 2019 VS 2023 VS 2030
Figure 16. Global AI-based Recommendation Engine Market Share by Region: 2023 VS 2030
Figure 17. Global AI-based Recommendation Engine Market Share by Players in 2023
Figure 18. Global Top AI-based Recommendation Engine Players by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in AI-based Recommendation Engine as of 2023)
Figure 19. The Top 10 and 5 Players Market Share by AI-based Recommendation Engine Revenue in 2023
Figure 20. North America AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 21. North America AI-based Recommendation Engine Market Share by Country (2019-2030)
Figure 22. United States AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 23. Canada AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 24. Europe AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 25. Europe AI-based Recommendation Engine Market Share by Country (2019-2030)
Figure 26. Germany AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 27. France AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 28. U.K. AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 29. Italy AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 30. Russia AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 31. Nordic Countries AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 32. Asia-Pacific AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 33. Asia-Pacific AI-based Recommendation Engine Market Share by Region (2019-2030)
Figure 34. China AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 35. Japan AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 36. South Korea AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 37. Southeast Asia AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 38. India AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 39. Australia AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 40. Latin America AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 41. Latin America AI-based Recommendation Engine Market Share by Country (2019-2030)
Figure 42. Mexico AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 43. Brazil AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 44. Middle East & Africa AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 45. Middle East & Africa AI-based Recommendation Engine Market Share by Country (2019-2030)
Figure 46. Turkey AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 47. Saudi Arabia AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 48. UAE AI-based Recommendation Engine Market Size YoY Growth (2019-2030) & (US$ Million)
Figure 49. Microsoft Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 50. Google Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 51. Andi Search Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 52. Metaphor AI Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 53. Brave Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 54. Phind Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 55. Perplexity AI Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 56. NeevaAI Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 57. Qubit Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 58. Dynamic Yield Revenue Growth Rate in AI-based Recommendation Engine Business (2019-2024)
Figure 59. Bottom-up and Top-down Approaches for This Report
Figure 60. Data Triangulation
Figure 61. Key Executives Interviewed
Published By : QY Research
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