Global AI-Based Recommendation System Market Growth (Status and Outlook) 2025-2031

The global AI-Based Recommendation System market size is predicted to grow from US$ 2133 million in 2025 to US$ 3363 million in 2031; it is expected to grow at a CAGR of 7.9% from 2025 to 2031.

An AI-based recommendation system is a type of software that uses artificial intelligence algorithms to analyze data on user behavior and preferences in order to suggest products, services, or content that the user is likely to be interested in. These systems are commonly used in e-commerce, entertainment, and social media platforms to enhance the user experience and increase engagement.

AI-based recommendation systems can be based on various types of algorithms, including collaborative filtering, content-based filtering, and hybrid models. Collaborative filtering analyzes user behavior and preferences to identify patterns and similarities in order to make recommendations. Content-based filtering, on the other hand, analyzes the features of products or content to recommend similar items to users based on their preferences.

Overall, AI-based recommendation systems have proved to be effective in improving user engagement, increasing sales, and reducing churn rates in various industries.

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.

LPI (LP Information)' newest research report, the “AI-Based Recommendation System Industry Forecast” looks at past sales and reviews total world AI-Based Recommendation System sales in 2024, providing a comprehensive analysis by region and market sector of projected AI-Based Recommendation System sales for 2025 through 2031. With AI-Based Recommendation System sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world AI-Based Recommendation System industry.

This Insight Report provides a comprehensive analysis of the global AI-Based Recommendation System landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on AI-Based Recommendation System portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global AI-Based Recommendation System market.

This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for AI-Based Recommendation System and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global AI-Based Recommendation System.

This report presents a comprehensive overview, market shares, and growth opportunities of AI-Based Recommendation System market by product type, application, key players and key regions and countries.

Segmentation by Type:
Collaborative Filtering
Content Based Filtering
Hybrid Recommendation

Segmentation by Application:
E-commerce Platform
Online Education
Social Networking
Finance
News and Media
Health Care
Travel
Other

This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries

The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
AWS
IBM
Google
SAP
Microsoft
Salesforce
Intel
HPE
Oracle
Sentient Technologies
Netflix
Facebook
Alibaba
Huawei
Tencent

Please note: The report will take approximately 2 business days to prepare and deliver.