Market Overview:
The global recommendation engine market is expected to grow at a CAGR of 16.5% during the forecast period from 2018 to 2030. The market growth can be attributed to the increasing demand for personalized recommendations and growing need for improved customer experience. The collaborative filtering segment is expected to hold the largest share of the global recommendation engine market in 2018, owing to its ability to recommend items that are similar or recommended by others.
Product Definition:
A recommendation engine (algorithm) is a computer program that suggests items for users of digital platforms such as e-commerce websites and social networks. It uses large data sets to create models of users' likes and interests, then recommends similar or recommended items to individual users.
Collaborative Filtering:
Collaborative filtering is a technique where multiple users or items are filtered based on some common criterion. The collaborative filter uses the rating or opinion of another user to improve its decision making process. This technology is used in various applications such as online shopping, personalized advertising, and social media site recommendation.
The growth factor for collaborative filtering in the Recommendation Engine market is High adoption of Internet services coupled with rising need for customized products.
Content-based Filtering:
The content-based filtering (CBF) is a technique to remove duplicate or similar documents from a large body of data. It uses the concept of statistical analysis of the document and its characteristics to eliminate similar results. The CBF process makes use of several filters that are applied in stages, starting with an initial filter that removes a certain percentage (usually around 75%)of all possible duplicates, followed by another filter that eliminates the remaining duplicates.
Application Insights:
The manufacturing application segment led the global recommendation engine market in 2017. The growth can be attributed to the increasing use of recommenders for improving customer engagement and business productivity. Furthermore, collaborative filtering provides a better solution for this application as it filters recommendations based on user preferences, thus providing a personalized experience to users.
The healthcare segment is expected to register significant growth over the forecast period owing to factors such as an increase in demand for effective information flow within organizations and increased usage of recommenders by hospitals and clinics across various applications such as patient engagement, clinical workflow management, employee performance management & development, etc. Moreover, recommenders are also being used by companies offering services related to medical equipment installation along with home healthcare monitoring devices that assist patients in maintaining optimum health conditions at home through remote monitoring systems supported by AI technology.
Regional Analysis:
North America dominated the global market in 2017. The growth can be attributed to the presence of a large number of industry players, increased adoption of advanced technologies such as AI and machine learning, and high demand for customized solutions. Moreover, increasing use cases such as customer relationship management (CRM), content recommendation, collaborative filtering among others are expected to drive the regional market over the forecast period.
Asia Pacific is anticipated to witness significant growth over the forecast period owing to increasing internet penetration rates coupled with growing smartphone adoption in emerging countries such as India and China. Furthermore, rising awareness about online shopping due to social media influence is further fueling regional growth. Additionally, government initiatives encouraging e-commerce activities are also contributing towards higher sales volumes in this region thereby driving overall revenue generation within Asia Pacific region.
Growth Factors:
- Increasing demand for personalized recommendations
- Growing number of online shoppers
- Proliferation of smart devices and internet connectivity
- Advances in artificial intelligence and machine learning algorithms
- Emergence of new business models such as subscription-based services
Scope Of The Report
Report Attributes
Report Details
Report Title
Recommendation Engine Market Research Report
By Type
Collaborative Filtering, Content-based Filtering, Hybrid Recommendation
By Application
Manufacturing, Healthcare, BFSI, Media and entertainment, Transportation, Others
By Companies
IBM, Google, AWS, Microsoft, Salesforce, Sentient Technologies, HPE, Oracle, Intel, SAP, Fuzzy.AI, Infinite Analytics
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
181
Number of Tables & Figures
127
Customization Available
Yes, the report can be customized as per your need.
Global Recommendation Engine Market Report Segments:
The global Recommendation Engine market is segmented on the basis of:
Types
Collaborative Filtering, Content-based Filtering, Hybrid Recommendation
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Manufacturing, Healthcare, BFSI, Media and entertainment, Transportation, Others
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- IBM
- Google
- AWS
- Microsoft
- Salesforce
- Sentient Technologies
- HPE
- Oracle
- Intel
- SAP
- Fuzzy.AI
- Infinite Analytics
Highlights of The Recommendation Engine Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- Collaborative Filtering
- Content-based Filtering
- Hybrid Recommendation
- By Application:
- Manufacturing
- Healthcare
- BFSI
- Media and entertainment
- Transportation
- Others
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Recommendation Engine Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
How you may use our products:
- Correctly Positioning New Products
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8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
Recommendation engines are a type of artificial intelligence that can be used to make recommendations for products or services. They are designed to learn about the user's preferences and use this information to provide suggestions.
Some of the major companies in the recommendation engine market are IBM, Google, AWS, Microsoft, Salesforce, Sentient Technologies, HPE, Oracle, Intel, SAP, Fuzzy.AI, Infinite Analytics.
The recommendation engine market is expected to grow at a compound annual growth rate of 16.5%.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Recommendation Engine Market Overview
4.1 Introduction
4.1.1 Market Taxonomy
4.1.2 Market Definition
4.1.3 Macro-Economic Factors Impacting the Market Growth
4.2 Recommendation Engine Market Dynamics
4.2.1 Market Drivers
4.2.2 Market Restraints
4.2.3 Market Opportunity
4.3 Recommendation Engine Market - Supply Chain Analysis
4.3.1 List of Key Suppliers
4.3.2 List of Key Distributors
4.3.3 List of Key Consumers
4.4 Key Forces Shaping the Recommendation Engine Market
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers
4.4.3 Threat of Substitution
4.4.4 Threat of New Entrants
4.4.5 Competitive Rivalry
4.5 Global Recommendation Engine Market Size & Forecast, 2018-2028
4.5.1 Recommendation Engine Market Size and Y-o-Y Growth
4.5.2 Recommendation Engine Market Absolute $ Opportunity
Chapter 5 Global Recommendation Engine Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Recommendation Engine Market Size Forecast by Type
5.2.1 Collaborative Filtering
5.2.2 Content-based Filtering
5.2.3 Hybrid Recommendation
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Recommendation Engine Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Recommendation Engine Market Size Forecast by Applications
6.2.1 Manufacturing
6.2.2 Healthcare
6.2.3 BFSI
6.2.4 Media and entertainment
6.2.5 Transportation
6.2.6 Others
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Recommendation Engine Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Recommendation Engine Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Recommendation Engine Analysis and Forecast
9.1 Introduction
9.2 North America Recommendation Engine Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Recommendation Engine Market Size Forecast by Type
9.6.1 Collaborative Filtering
9.6.2 Content-based Filtering
9.6.3 Hybrid Recommendation
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Recommendation Engine Market Size Forecast by Applications
9.10.1 Manufacturing
9.10.2 Healthcare
9.10.3 BFSI
9.10.4 Media and entertainment
9.10.5 Transportation
9.10.6 Others
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Recommendation Engine Analysis and Forecast
10.1 Introduction
10.2 Europe Recommendation Engine Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Recommendation Engine Market Size Forecast by Type
10.6.1 Collaborative Filtering
10.6.2 Content-based Filtering
10.6.3 Hybrid Recommendation
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Recommendation Engine Market Size Forecast by Applications
10.10.1 Manufacturing
10.10.2 Healthcare
10.10.3 BFSI
10.10.4 Media and entertainment
10.10.5 Transportation
10.10.6 Others
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Recommendation Engine Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Recommendation Engine Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Recommendation Engine Market Size Forecast by Type
11.6.1 Collaborative Filtering
11.6.2 Content-based Filtering
11.6.3 Hybrid Recommendation
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Recommendation Engine Market Size Forecast by Applications
11.10.1 Manufacturing
11.10.2 Healthcare
11.10.3 BFSI
11.10.4 Media and entertainment
11.10.5 Transportation
11.10.6 Others
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Recommendation Engine Analysis and Forecast
12.1 Introduction
12.2 Latin America Recommendation Engine Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Recommendation Engine Market Size Forecast by Type
12.6.1 Collaborative Filtering
12.6.2 Content-based Filtering
12.6.3 Hybrid Recommendation
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Recommendation Engine Market Size Forecast by Applications
12.10.1 Manufacturing
12.10.2 Healthcare
12.10.3 BFSI
12.10.4 Media and entertainment
12.10.5 Transportation
12.10.6 Others
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Recommendation Engine Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Recommendation Engine Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Recommendation Engine Market Size Forecast by Type
13.6.1 Collaborative Filtering
13.6.2 Content-based Filtering
13.6.3 Hybrid Recommendation
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Recommendation Engine Market Size Forecast by Applications
13.10.1 Manufacturing
13.10.2 Healthcare
13.10.3 BFSI
13.10.4 Media and entertainment
13.10.5 Transportation
13.10.6 Others
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Recommendation Engine Market: Competitive Dashboard
14.2 Global Recommendation Engine Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 IBM
14.3.2 Google
14.3.3 AWS
14.3.4 Microsoft
14.3.5 Salesforce
14.3.6 Sentient Technologies
14.3.7 HPE
14.3.8 Oracle
14.3.9 Intel
14.3.10 SAP
14.3.11 Fuzzy.AI
14.3.12 Infinite Analytics
