查看購物車
  • Simplified Chinese
  • English
  • Japanese
封面
市場調查報告書
商品編碼
1826666

全球內容推薦引擎市場規模研究與預測，按組件、按過濾方法、按組織規模和區域預測 2025-2035

Global Content Recommendation Engine Market Size Study & Forecast, by Component, by Filtering Approach, by Organization Size and Regional Forecasts 2025-2035
出版日期: | 出版商: Bizwit Research & Consulting LLP | 英文 285 Pages | 商品交期: 2-3個工作天內

價格
簡介目錄

市場定義與概述

2024 年全球內容推薦引擎市場價值約為 84.2 億美元，預計在 2025-2035 年預測期內將以 28.50% 的複合年成長率擴張，到 2035 年最終達到 1328.1 億美元。內容推薦引擎是一個複雜的系統，它利用人工智慧 (AI)、機器學習 (ML) 和預測分析，為跨數位平台的使用者提供個人化建議。透過分析偏好、搜尋歷史記錄、瀏覽模式和購買行為等大量消費者資料，這些引擎不僅可以增強用戶參與度，還可以推動企業的獲利策略。數位媒體消費的快速成長、電子商務活動的激增以及企業越來越依賴數據驅動的個人化來改善客戶體驗和保留率，推動了對此類系統的需求。

各行各業數位轉型的加速，加速了推薦引擎的應用。零售、娛樂、金融服務和保險業（BFSI）以及醫療保健等領域的公司正在將這些系統整合到其平台中，以提升交叉銷售、追加銷售和客戶互動。根據業界洞察，擁有先進推薦系統的平台報告稱，用戶參與度提升了30%，轉換率也顯著提升。此外，雲端運算和即時分析與建議技術的融合，正在拓寬應用範圍並降低部署複雜性。然而，資料隱私問題以及消費者資料道德使用的監管框架等挑戰構成了一定的阻礙因素，可能會在未來幾年阻礙市場的成長步伐。

報告中包含的詳細部門和子部門如下：

目錄

第 1 章：全球內容推薦引擎市場報告範圍與方法

  • 研究目標
  • 研究方法
    • 預測模型
    • 案頭研究
    • 自上而下和自下而上的方法
  • 研究屬性
  • 研究範圍
    • 市場定義
    • 市場區隔
  • 研究假設
    • 包容與排斥
    • 限制
    • 研究涵蓋的年份

第2章：摘要整理

  • CEO/CXO 立場
  • 戰略洞察
  • ESG分析
  • 主要發現

第3章：全球內容推薦引擎市場力量分析

  • 塑造全球內容推薦引擎市場的市場力量（2024-2035）
  • 推動因素
    • 數位媒體消費呈指數級成長
    • 電子商務活動激增
  • 限制
    • 資料隱私問題
  • 機會
    • 企業越來越依賴數據驅動的個人化

第4章：全球內容推薦引擎產業分析

  • 波特五力模型
    • 買方議價能力
    • 供應商的議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭對手
  • 波特五力預測模型（2024-2035）
  • PESTEL分析
    • 政治的
    • 經濟
    • 社會的
    • 科技
    • 環境的
    • 合法的
  • 最佳投資機會
  • 最佳制勝策略（2025年）
  • 市佔率分析（2024-2025）
  • 2025年全球定價分析與趨勢
  • 分析師建議與結論

第5章：全球內容推薦引擎市場規模與預測：按組件 - 2025-2035

  • 市場概況
  • 全球內容推薦引擎市場表現-潛力分析（2025年）
  • 解決方案

第6章：全球內容推薦引擎市場規模與預測：按過濾方法 - 2025-2035

  • 市場概況
  • 全球內容推薦引擎市場表現-潛力分析（2025年）
  • 協同過濾
  • 基於內容的過濾

第7章：全球內容推薦引擎市場規模與預測：依組織規模 - 2025-2035

  • 市場概況
  • 全球內容推薦引擎市場表現-潛力分析（2025年）
  • 中小企業
  • 大型企業

第 8 章：全球內容推薦引擎市場規模與預測：按地區 - 2025-2035 年

  • 成長區域市場簡介
  • 領先國家和新興國家
  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 西班牙
    • 義大利
    • 歐洲其他地區
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 亞太地區其他地區
  • 拉丁美洲
    • 巴西
    • 墨西哥
  • 中東和非洲
    • 阿拉伯聯合大公國
    • 沙烏地阿拉伯（KSA）
    • 南非

第9章：競爭情報

  • 頂級市場策略
  • Amazon Web Services Inc.
    • 公司概況
    • 主要高階主管
    • 公司簡介
    • 財務表現（視數據可用性而定）
    • 產品/服務端口
    • 近期發展
    • 市場策略
    • SWOT分析
  • Google LLC
  • Microsoft Corporation
  • IBM Corporation
  • Oracle Corporation
  • Salesforce Inc.
  • Adobe Inc.
  • SAP SE
  • Intel Corporation
  • Hewlett Packard Enterprise Development LP
  • Tata Consultancy Services Limited
  • Infosys Limited
  • Accenture Plc
  • SAS Institute Inc.
  • Netflix Inc.
簡介目錄

Market Definition and Overview

The Global Content Recommendation Engine Market is valued at approximately USD 8.42 billion in 2024 and is expected to expand at a CAGR of 28.50% during the forecast period of 2025-2035, ultimately reaching USD 132.81 billion by 2035. A content recommendation engine is a sophisticated system that leverages artificial intelligence (AI), machine learning (ML), and predictive analytics to deliver personalized suggestions to users across digital platforms. By analyzing vast streams of consumer data such as preferences, search history, browsing patterns, and purchasing behavior, these engines not only enhance user engagement but also drive monetization strategies for enterprises. The demand for such systems is being driven by exponential growth in digital media consumption, a surge in e-commerce activities, and the increasing reliance of businesses on data-driven personalization to improve customer experience and retention.

The accelerated digital transformation across industries has intensified the adoption of recommendation engines. Companies spanning retail, entertainment, BFSI, and healthcare are integrating these systems into their platforms to elevate cross-selling, upselling, and customer engagement initiatives. According to industry insights, platforms with advanced recommendation systems have reported up to 30% increases in user engagement and a marked improvement in conversion rates. Furthermore, the integration of cloud computing and real-time analytics into recommendation technologies is broadening the scope of applications and reducing deployment complexities. Nonetheless, challenges such as data privacy concerns and regulatory frameworks regarding the ethical use of consumer data pose certain restraints that may impede the pace of market growth in the coming years.

The detailed segments and sub-segments included in the report are:

By Component:

  • Solution

By Filtering Approach:

  • Collaborative Filtering
  • Content-Based Filtering

By Organization Size:

  • Small & Medium Enterprises (SMEs)
  • Large Enterprises

By Region:

  • North America
  • U.S.
  • Canada
  • Europe
  • UK
  • Germany
  • France
  • Spain
  • Italy
  • ROE
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • RoAPAC
  • Latin America
  • Brazil
  • Mexico
  • Middle East & Africa
  • UAE
  • Saudi Arabia
  • South Africa
  • Rest of Middle East & Africa
  • Segment Insights
  • Collaborative filtering is anticipated to dominate the global content recommendation engine market throughout the forecast period. This approach capitalizes on user behavior patterns and community data to generate accurate predictions, making it especially effective for e-commerce platforms, video-on-demand services, and digital retail applications. As enterprises strive to replicate the seamless personalization experiences of global leaders such as Amazon and Netflix, collaborative filtering stands out as the cornerstone technology driving deeper customer connections and repeat interactions.
  • From a revenue contribution perspective, large enterprises currently lead the market. With their expansive customer bases and vast data ecosystems, these organizations are in a unique position to maximize the return on investment from recommendation systems. Enterprises in industries such as streaming, banking, and retail have been quick to scale solutions that enhance lifetime customer value, improve recommendation accuracy, and strengthen competitive positioning. Meanwhile, SMEs, powered by cloud-based and cost-efficient solutions, are rapidly catching up as accessibility to sophisticated recommendation platforms widens.
  • The Global Content Recommendation Engine Market exhibits notable geographic trends. North America accounted for the largest market share in 2025, underpinned by strong adoption across media and entertainment, retail, and IT sectors, along with the region's early embrace of AI-driven personalization. Europe follows closely, driven by its growing e-commerce penetration and regulatory compliance with GDPR, which has accelerated the adoption of transparent and ethical recommendation solutions. The Asia Pacific region is expected to witness the fastest growth over the forecast period, propelled by rapid digitalization, increasing smartphone penetration, and booming demand for streaming and e-commerce platforms in China, India, and Southeast Asia. Government-backed digital initiatives and robust startup ecosystems in the region are further augmenting growth prospects.

Major market players included in this report are:

  • Amazon Web Services Inc.
  • Google LLC
  • Microsoft Corporation
  • IBM Corporation
  • Oracle Corporation
  • Salesforce Inc.
  • Adobe Inc.
  • SAP SE
  • Intel Corporation
  • Hewlett Packard Enterprise Development LP
  • Tata Consultancy Services Limited
  • Infosys Limited
  • Accenture Plc
  • SAS Institute Inc.
  • Netflix Inc.

Global Content Recommendation Engine Market Report Scope:

  • Historical Data - 2023, 2024
  • Base Year for Estimation - 2024
  • Forecast period - 2025-2035
  • Report Coverage - Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
  • Regional Scope - North America; Europe; Asia Pacific; Latin America; Middle East & Africa
  • Customization Scope - Free report customization (equivalent to up to 8 analysts' working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values for the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within the countries involved in the study. The report also provides detailed information about crucial aspects, such as driving factors and challenges, which will define the future growth of the market. Additionally, it incorporates potential opportunities in micro-markets for stakeholders to invest, along with a detailed analysis of the competitive landscape and product offerings of key players. The detailed segments and sub-segments of the market are explained below:

Key Takeaways:

  • Market Estimates & Forecast for 10 years from 2025 to 2035.
  • Annualized revenues and regional-level analysis for each market segment.
  • Detailed analysis of the geographical landscape with country-level analysis of major regions.
  • Competitive landscape with information on major players in the market.
  • Analysis of key business strategies and recommendations on future market approach.
  • Analysis of the competitive structure of the market.
  • Demand side and supply side analysis of the market.

Table of Contents

Chapter 1. Global Content Recommendation Engine Market Report Scope & Methodology

  • 1.1. Research Objective
  • 1.2. Research Methodology
    • 1.2.1. Forecast Model
    • 1.2.2. Desk Research
    • 1.2.3. Top Down and Bottom-Up Approach
  • 1.3. Research Attributes
  • 1.4. Scope of the Study
    • 1.4.1. Market Definition
    • 1.4.2. Market Segmentation
  • 1.5. Research Assumption
    • 1.5.1. Inclusion & Exclusion
    • 1.5.2. Limitations
    • 1.5.3. Years Considered for the Study

Chapter 2. Executive Summary

  • 2.1. CEO/CXO Standpoint
  • 2.2. Strategic Insights
  • 2.3. ESG Analysis
  • 2.4. key Findings

Chapter 3. Global Content Recommendation Engine Market Forces Analysis

  • 3.1. Market Forces Shaping The Global Content Recommendation Engine Market (2024-2035)
  • 3.2. Drivers
    • 3.2.1. exponential growth in digital media consumption
    • 3.2.2. a surge in e-commerce activities
  • 3.3. Restraints
    • 3.3.1. data privacy concerns
  • 3.4. Opportunities
    • 3.4.1. increasing reliance of businesses on data-driven personalization

Chapter 4. Global Content Recommendation Engine Industry Analysis

  • 4.1. Porter's 5 Forces Model
    • 4.1.1. Bargaining Power of Buyer
    • 4.1.2. Bargaining Power of Supplier
    • 4.1.3. Threat of New Entrants
    • 4.1.4. Threat of Substitutes
    • 4.1.5. Competitive Rivalry
  • 4.2. Porter's 5 Force Forecast Model (2024-2035)
  • 4.3. PESTEL Analysis
    • 4.3.1. Political
    • 4.3.2. Economical
    • 4.3.3. Social
    • 4.3.4. Technological
    • 4.3.5. Environmental
    • 4.3.6. Legal
  • 4.4. Top Investment Opportunities
  • 4.5. Top Winning Strategies (2025)
  • 4.6. Market Share Analysis (2024-2025)
  • 4.7. Global Pricing Analysis And Trends 2025
  • 4.8. Analyst Recommendation & Conclusion

Chapter 5. Global Content Recommendation Engine Market Size & Forecasts by Component 2025-2035

  • 5.1. Market Overview
  • 5.2. Global Content Recommendation Engine Market Performance - Potential Analysis (2025)
  • 5.3. Solution
    • 5.3.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
    • 5.3.2. Market size analysis, by region, 2025-2035

Chapter 6. Global Content Recommendation Engine Market Size & Forecasts by Filtering approach 2025-2035

  • 6.1. Market Overview
  • 6.2. Global Content Recommendation Engine Market Performance - Potential Analysis (2025)
  • 6.3. Collaborative Filtering
    • 6.3.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
    • 6.3.2. Market size analysis, by region, 2025-2035
  • 6.4. Content-Based Filtering
    • 6.4.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
    • 6.4.2. Market size analysis, by region, 2025-2035

Chapter 7. Global Content Recommendation Engine Market Size & Forecasts by Organization size 2025-2035

  • 7.1. Market Overview
  • 7.2. Global Content Recommendation Engine Market Performance - Potential Analysis (2025)
  • 7.3. Small & Medium Enterprises (SMEs)
    • 7.3.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
    • 7.3.2. Market size analysis, by region, 2025-2035
  • 7.4. Large Enterprises
    • 7.4.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
    • 7.4.2. Market size analysis, by region, 2025-2035

Chapter 8. Global Content Recommendation Engine Market Size & Forecasts by Region 2025-2035

  • 8.1. Growth Content Recommendation Engine Market, Regional Market Snapshot
  • 8.2. Top Leading & Emerging Countries
  • 8.3. North America Content Recommendation Engine Market
    • 8.3.1. U.S. Content Recommendation Engine Market
      • 8.3.1.1. Component breakdown size & forecasts, 2025-2035
      • 8.3.1.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.3.1.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.3.2. Canada Content Recommendation Engine Market
      • 8.3.2.1. Component breakdown size & forecasts, 2025-2035
      • 8.3.2.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.3.2.3. Organization size breakdown size & forecasts, 2025-2035
  • 8.4. Europe Content Recommendation Engine Market
    • 8.4.1. UK Content Recommendation Engine Market
      • 8.4.1.1. Component breakdown size & forecasts, 2025-2035
      • 8.4.1.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.4.1.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.4.2. Germany Content Recommendation Engine Market
      • 8.4.2.1. Component breakdown size & forecasts, 2025-2035
      • 8.4.2.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.4.2.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.4.3. France Content Recommendation Engine Market
      • 8.4.3.1. Component breakdown size & forecasts, 2025-2035
      • 8.4.3.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.4.3.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.4.4. Spain Content Recommendation Engine Market
      • 8.4.4.1. Component breakdown size & forecasts, 2025-2035
      • 8.4.4.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.4.4.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.4.5. Italy Content Recommendation Engine Market
      • 8.4.5.1. Component breakdown size & forecasts, 2025-2035
      • 8.4.5.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.4.5.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.4.6. Rest of Europe Content Recommendation Engine Market
      • 8.4.6.1. Component breakdown size & forecasts, 2025-2035
      • 8.4.6.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.4.6.3. Organization size breakdown size & forecasts, 2025-2035
  • 8.5. Asia Pacific Content Recommendation Engine Market
    • 8.5.1. China Content Recommendation Engine Market
      • 8.5.1.1. Component breakdown size & forecasts, 2025-2035
      • 8.5.1.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.5.1.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.5.2. India Content Recommendation Engine Market
      • 8.5.2.1. Component breakdown size & forecasts, 2025-2035
      • 8.5.2.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.5.2.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.5.3. Japan Content Recommendation Engine Market
      • 8.5.3.1. Component breakdown size & forecasts, 2025-2035
      • 8.5.3.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.5.3.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.5.4. Australia Content Recommendation Engine Market
      • 8.5.4.1. Component breakdown size & forecasts, 2025-2035
      • 8.5.4.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.5.4.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.5.5. South Korea Content Recommendation Engine Market
      • 8.5.5.1. Component breakdown size & forecasts, 2025-2035
      • 8.5.5.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.5.5.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.5.6. Rest of APAC Content Recommendation Engine Market
      • 8.5.6.1. Component breakdown size & forecasts, 2025-2035
      • 8.5.6.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.5.6.3. Organization size breakdown size & forecasts, 2025-2035
  • 8.6. Latin America Content Recommendation Engine Market
    • 8.6.1. Brazil Content Recommendation Engine Market
      • 8.6.1.1. Component breakdown size & forecasts, 2025-2035
      • 8.6.1.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.6.1.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.6.2. Mexico Content Recommendation Engine Market
      • 8.6.2.1. Component breakdown size & forecasts, 2025-2035
      • 8.6.2.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.6.2.3. Organization size breakdown size & forecasts, 2025-2035
  • 8.7. Middle East and Africa Content Recommendation Engine Market
    • 8.7.1. UAE Content Recommendation Engine Market
      • 8.7.1.1. Component breakdown size & forecasts, 2025-2035
      • 8.7.1.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.7.1.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.7.2. Saudi Arabia (KSA) Content Recommendation Engine Market
      • 8.7.2.1. Component breakdown size & forecasts, 2025-2035
      • 8.7.2.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.7.2.3. Organization size breakdown size & forecasts, 2025-2035
    • 8.7.3. South Africa Content Recommendation Engine Market
      • 8.7.3.1. Component breakdown size & forecasts, 2025-2035
      • 8.7.3.2. Filtering approach breakdown size & forecasts, 2025-2035
      • 8.7.3.3. Organization size breakdown size & forecasts, 2025-2035

Chapter 9. Competitive Intelligence

  • 9.1. Top Market Strategies
  • 9.2. Amazon Web Services Inc.
    • 9.2.1. Company Overview
    • 9.2.2. Key Executives
    • 9.2.3. Company Snapshot
    • 9.2.4. Financial Performance (Subject to Data Availability)
    • 9.2.5. Product/Services Port
    • 9.2.6. Recent Development
    • 9.2.7. Market Strategies
    • 9.2.8. SWOT Analysis
  • 9.3. Google LLC
  • 9.4. Microsoft Corporation
  • 9.5. IBM Corporation
  • 9.6. Oracle Corporation
  • 9.7. Salesforce Inc.
  • 9.8. Adobe Inc.
  • 9.9. SAP SE
  • 9.10. Intel Corporation
  • 9.11. Hewlett Packard Enterprise Development LP
  • 9.12. Tata Consultancy Services Limited
  • 9.13. Infosys Limited
  • 9.14. Accenture Plc
  • 9.15. SAS Institute Inc.
  • 9.16. Netflix Inc.