OTT 추천 엔진 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the OTT recommendation engine market size is projected to expand from USD 0.90 billion in 2025 and USD 1.17 billion in 2026 to USD 2.48 billion by 2031, registering a CAGR of 16.21% between 2026 to 2031.

This report is Segmented by Deployment Mode (Cloud-Based, On-Premises, and More), End User (Broadcasters and Pay-TV Operators, Digital Media and Publishing Platforms, and More), Business Model (Recurring Subscription Billing (SVOD), and More), Application (Content Recommendation, Advertising and Promotional Recommendation, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global OTT Recommendation Engine Market Trends and Insights

Demand for Hyper-Personalized Viewer Experiences

Recommendation quality has become a central operating measure because it affects churn, viewing time, and advertising yield at the same time. Netflix research presented in 2026 found that its recommendation system affects both title choice and whether viewers watch at all, linking personalization to revenue per user rather than only interface quality. The OTT recommendation engine market is therefore moving toward systems that coordinate home-page layout, artwork selection, and editorial presentation together. Netflix reported that its GenPage system improved core engagement measures and reduced end-to-end serving latency by 20% compared with its earlier multi-stage recommender. JioHotstar reported that more than 60% of users selected voice when text and voice discovery were both offered, showing that discovery interfaces can shape the value delivered by the underlying model.

Rising Streaming Content Volumes

The OTT recommendation engine market is supported by catalog growth that makes manual discovery increasingly difficult. Amagi tracked more than 6,500 FAST channel deliveries on its THUNDERSTORM platform by mid-2026, increasing the need for accurate program metadata and search results. A 2026 Hub Entertainment Research report found that incomplete genre, rating, or episode data can reduce a program's visibility in search and weaken contextual advertising matches. Metadata enrichment is becoming as important as ranking quality because poor metadata can leave useful catalog titles unseen. ThinkAnalytics introduced ThinkMetadataAI at IBC2025 to automate content tagging across dozens of languages and connect that information to its discovery tools. Providers that combine enriched metadata with ranking systems are better placed to serve platforms seeking stronger discovery across large and varied libraries.

Data Privacy and Consent Management Requirements

Privacy and transparency obligations are making cross-border personalization programs more difficult to operate. The European Commission states that Article 50 transparency rules under the EU AI Act will apply from August 2, 2026, with disclosure and documentation obligations for relevant systems that interact with people. The Digital Services Act also affects services that use algorithmic curation by requiring meaningful options beyond profiling-based recommendations. The OTT recommendation engine market must therefore support clearer consent controls, documentation, and user-facing explanations. Separate training environments for data subject to European privacy rules can increase infrastructure costs and slow model updates. Larger providers with established consent and governance systems may find it easier to meet these requirements than smaller specialist vendors.

Other drivers and restraints analyzed in the detailed report include:

1. First-Party Data Strategies in a Cookieless Advertising Environment
2. Real-Time and Edge-AI Inference for Contextual Recommendations
3. Cold-Start and Sparse-Interaction Limitations

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Cloud-based systems held 65.70% of the OTT recommendation engine market share in 2025 and are projected to grow at a 16.71% CAGR through 2031. Cloud delivery reduces the effort required to maintain feature stores, model retraining, and inference services for broadcaster-scale content libraries. It also allows teams to add capacity without building and operating the full underlying infrastructure. ByteDance's Volcengine offers a managed large-screen recommendation service for IPTV and OTT operators across home pages, channels, and content-detail pages. The service illustrates how the OTT recommendation engine market can offer packaged personalization to operators that do not maintain proprietary model teams.

On-premises systems remain relevant for state-owned broadcasters and operators subject to strict data-localization rules. These requirements are particularly important where cross-border transfer of viewer data is restricted. Hybrid arrangements are gaining attention among larger pay-TV operators that retain sensitive subscriber profiles within their own systems. Those operators can still use cloud capacity for less sensitive inference tasks and nonpersonal signals. This approach gives providers in the OTT recommendation engine market a way to preserve local control while providing flexible capacity without a fully isolated recommendation environment.

Broadcasters and pay-TV operators held 40.45% of revenue in 2025 within the OTT recommendation engine market. Their large content catalogs require effective discovery tools to make premium programs easier to find. As traditional broadcasters adopt streaming-led distribution, recommendation systems are becoming a retention tool rather than an optional app feature. Digital media and publishing platforms also use recommendation systems to manage high content volumes across news, podcasts, and user-generated material. Their use cases add demand for re-engagement and timely content presentation.

Connected-TV and device ecosystems are projected to grow at a 16.94% CAGR through 2031. Smart-TV manufacturers are placing recommendation features in the operating-system layer, allowing discovery to begin before a viewer enters an individual streaming app. In February 2026, Samsung partnered with Gracenote on LLM-enabled conversational search and discovery for its global smart-TV platform. Such integrations move discovery upstream toward the device interface. Device manufacturers can then influence audience attention, household-level discovery, and the advertising inventory associated with the home screen, which increases the importance of interoperability across the OTT recommendation engine market.

Complete Report Scope:

• By Deployment Mode
  • Cloud-Based
  • On-Premises
  • Hybrid Deployment
• By End User
  • Broadcasters and Pay-TV Operators
  • Digital Media and Publishing Platforms
  • Connected-TV and Device Ecosystems
• By Business Model
  • Recurring Subscription Billing (SVOD)
  • Transactional Billing (TVOD/PPV)
  • Advertising-Supported Billing (AVOD/FAST)
  • Hybrid Monetization Billing
• By Application
  • Content Recommendation
  • Advertising and Promotional Recommendation
  • Commerce and Subscription Recommendation
  • Other Applications
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Russia
    • Rest of Europe
  • Asia-Pacific
    • China
    • India
    • Japan
    • South Korea
    • Singapore
    • Australia
    • Rest of Asia-Pacific
  • Middle East
    • United Arab Emirates
    • Saudi Arabia
    • Turkey
    • Rest of Middle East
  • Africa
    • South Africa
    • Nigeria
    • Egypt
    • Rest of Africa

Geography Analysis

North America held 40.45% of global revenue in 2025, giving the region the largest global revenue position. The region has a dense base of subscription and ad-supported streaming platforms, established programmatic advertising tools, and significant connected-TV usage. Amazon's July 2026 AI-focused redesign work on Prime Video showed that recommendation investment has moved beyond product teams to senior leadership priorities. Amagi reported that the United States and Canada generated 54% of global FAST viewing hours and 74% of global FAST advertising impressions in mid-2026. Roku found that 64% of Roku households stream FAST content, supporting the importance of operating-system-level discovery. In South America, SKY+ and DGO deployed Mediagenix and Spideo's recommendation engine across a 10,000-title catalog in April 2025, covering 170 and 260 live channels, respectively.

Asia-Pacific is projected to record the highest regional CAGR of 16.78% through 2031. India, China, South Korea, Japan, and Southeast Asian markets combine mobile-first usage with expanding vernacular-language catalogs. JioHotstar serves more than 200 million users and reported that over 60% of users chose voice-based discovery when voice and text options were available. DMM TV has developed measurement methods that distinguish views driven by recommendation rows from views arriving through other platform paths. China applies the YD/T 4886-2024 standard for AI-based mobile video recommendation services, which took effect in October 2024 and defines technical and functional requirements for relevant services.

Europe faces higher deployment requirements because the EU AI Act, GDPR, and Digital Services Act increase the need for consent controls and explainable recommendation practices. Those requirements can raise engineering costs, but they also increase demand for providers with transparent personalization tools. In the Middle East, Sharjah Broadcasting Authority deployed Mediagenix and Spideo technology for the Maraya platform in May 2025, reporting engagement gains of 20% to 60% and a 35% improvement in conversion. Africa remains earlier in streaming adoption, though Mangomolo expanded its AI personalization and localization capabilities through 2025 and 2026 for clients that include the South African Broadcasting Corporation. The OTT recommendation engine market in these regions is shaped by the need to support local languages, regional content rights, and differing regulatory expectations.

1. Amazon Web Services, Inc.
2. Google LLC
3. Microsoft Corporation
4. Netflix, Inc.
5. Adobe Inc.
6. ThinkAnalytics Ltd.
7. Gracenote, Inc.
8. ContentWise S.r.l.
9. Recombee s.r.o.
10. Dynamic Yield Ltd.
11. Coveo Solutions Inc.
12. Salesforce, Inc.
13. Oracle Corporation
14. SAP SE
15. Algolia SAS
16. Optimizely, Inc.
17. Bloomreach Inc.
18. Taboola, Inc.
19. Outbrain Inc.
20. Muvi LLC
21. 24i Media Group B.V.
22. Mediagenix NV
23. Spideo SAS
24. Argoid Analytics India Private Limited
25. Kaltura, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Impact of Macroeconomic Factors on the Market
• 4.3 Market Drivers
  • 4.3.1 Rising Streaming-Content Volumes
  • 4.3.2 Demand for Hyper-Personalized Viewer Experiences
  • 4.3.3 First-Party Data Strategies in a Cookieless Advertising Environment
  • 4.3.4 Real-Time and Edge-AI Inference for Contextual Recommendations
  • 4.3.5 Multilingual Content Expansion in Emerging Markets
  • 4.3.6 Recommendation-Led Optimization of Retention and Advertising Yield
• 4.4 Market Restraints
  • 4.4.1 Data Privacy and Consent Management Requirements
  • 4.4.2 Cold-Start and Sparse-Interaction Limitations
  • 4.4.3 Algorithmic Bias, Filter Bubbles, and Explainability Gaps
  • 4.4.4 Escalating Compute, Feature-Store, and Model-Serving Costs
• 4.5 Industry Value-Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces
  • 4.8.1 Threat of New Entrants Analysis
  • 4.8.2 Bargaining Power of Suppliers Analysis
  • 4.8.3 Bargaining Power of Buyers Analysis
  • 4.8.4 Threat of Substitutes Analysis
  • 4.8.5 Competitive Rivalry Analysis

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Deployment Mode
  • 5.1.1 Cloud-Based
  • 5.1.2 On-Premises
  • 5.1.3 Hybrid Deployment
• 5.2 By End User
  • 5.2.1 Broadcasters and Pay-TV Operators
  • 5.2.2 Digital Media and Publishing Platforms
  • 5.2.3 Connected-TV and Device Ecosystems
• 5.3 By Business Model
  • 5.3.1 Recurring Subscription Billing (SVOD)
  • 5.3.2 Transactional Billing (TVOD/PPV)
  • 5.3.3 Advertising-Supported Billing (AVOD/FAST)
  • 5.3.4 Hybrid Monetization Billing
• 5.4 By Application
  • 5.4.1 Content Recommendation
  • 5.4.2 Advertising and Promotional Recommendation
  • 5.4.3 Commerce and Subscription Recommendation
  • 5.4.4 Other Applications
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 United Kingdom
    • 5.5.3.2 Germany
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Spain
    • 5.5.3.6 Russia
    • 5.5.3.7 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 India
    • 5.5.4.3 Japan
    • 5.5.4.4 South Korea
    • 5.5.4.5 Singapore
    • 5.5.4.6 Australia
    • 5.5.4.7 Rest of Asia-Pacific
  • 5.5.5 Middle East
    • 5.5.5.1 United Arab Emirates
    • 5.5.5.2 Saudi Arabia
    • 5.5.5.3 Turkey
    • 5.5.5.4 Rest of Middle East
  • 5.5.6 Africa
    • 5.5.6.1 South Africa
    • 5.5.6.2 Nigeria
    • 5.5.6.3 Egypt
    • 5.5.6.4 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Amazon Web Services, Inc.
  • 6.4.2 Google LLC
  • 6.4.3 Microsoft Corporation
  • 6.4.4 Netflix, Inc.
  • 6.4.5 Adobe Inc.
  • 6.4.6 ThinkAnalytics Ltd.
  • 6.4.7 Gracenote, Inc.
  • 6.4.8 ContentWise S.r.l.
  • 6.4.9 Recombee s.r.o.
  • 6.4.10 Dynamic Yield Ltd.
  • 6.4.11 Coveo Solutions Inc.
  • 6.4.12 Salesforce, Inc.
  • 6.4.13 Oracle Corporation
  • 6.4.14 SAP SE
  • 6.4.15 Algolia SAS
  • 6.4.16 Optimizely, Inc.
  • 6.4.17 Bloomreach Inc.
  • 6.4.18 Taboola, Inc.
  • 6.4.19 Outbrain Inc.
  • 6.4.20 Muvi LLC
  • 6.4.21 24i Media Group B.V.
  • 6.4.22 Mediagenix NV
  • 6.4.23 Spideo SAS
  • 6.4.24 Argoid Analytics India Private Limited
  • 6.4.25 Kaltura, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment