as well as doing more with significantly less. Increasing focus on enhancing customer experience, rapid
digitalization, and need for analyzing large volumes of customer data are factors driving the market
across the globe. It is essential to understand that Content Recommendation Engine includes several
minor technologies like deep learning, machine learning, robotics, etc.
Global Content Recommendation Engines market report covers segmentation analysis of Component,
Filtering Approach and Vertical. Component segment of Content Recommendation Engines further
classifies into Solution and Service. Report further covers segments of Filtering Approach of Content
Recommendation Engines which includes Collaborative, Content-Based, Hybrid. Report further covers
segments of Vertical of Content Recommendation Engines which includes E-commerce, Media,
Entertainment & Gaming, Retail, Hospitality, IT & Telecommunication, BFSI, Education & training,
Healthcare & Pharmaceutical, Others .
The regional analysis of Global Content Recommendation Engine Market is considered for the key
regions such as Asia Pacific, North America, Europe, Latin America and Rest of the World. North America
is the fastest growing region across the world in terms of market share. Whereas, owing to the countries
such as China, Japan, and India, Asia Pacific region is anticipated to be the dominating region over the
forecast period 2019-2023.
Major Players profiled in the Content Recommendation Engine Market report incorporate: Amazon
Web Services (US), Boomtrain (US), Certona (US), Curata (US), Cxense (Norway), Dynamic Yield (US), IBM
(US), Kibo Commerce (US), Outbrain (US), Revcontent (US), Taboola (US) , ThinkAnalytics (UK)
Latest Industry News:
AWS announced Amazon Personalize, which allows you to get your first recommendation engine
running quickly, to deliver immediate value to your end user or business. As your understanding
increases (or if you are already familiar with data science), you can take advantage of the deep
capabilities of Amazon Personalize to improve your recommendations.
The most well-known and successful ML use cases have been retail websites, music streaming apps, and
social media platforms. For years, they’ve been embedding ML technologies into the heart of their user
experience. They commonly provide each user with an individual personalized recommendation, based
on both historic data points and real-time activity (such as click data).
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The objective of the study is to define market sizes of different segments & countries in recent years and
to forecast the values to the coming eight years. The report is designed to incorporate both qualitative
and quantitative aspects of the industry within each of the regions and countries involved in the study.
Furthermore, the report also caters the detailed information about the crucial aspects such as driving
factors & challenges which will define the future growth of the market. Additionally, the report shall also