Content Recommendation

Get user-focused recommendations that personalize the user experience by delivering contextual content. Ontotext’s Content Recommendation provides suggestions for relevant content based on natural language processing, search history, user profiles and semantically enriched data.

Key Features:

  • Personalized recommendations
  • Contextual recommendations
  • Scalable & efficient
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How You’ll Benefit

Increase the engagement of your users

Generate personalized content streams

Provide your authors with the ability to find relevant content

How it Works

Content suggestions

By leveraging the documents’ semantic fingerprints extracted by the Concept Extraction Service, The Recommendation Service efficiently suggests relevant related content.

Behavioral recommendations

The quality of the Recommendation Service is further enhanced by custom tailored behavioral recommendations, based on the actions of the readers and their profiles.

Standard Solution vs Custom Solution

Solutions Comparison Standard
Contextual Recommendation
Behavioural Recommendation
Hybrid Algorithms
Out of the Box Semantic Tagging
Integration with Click/Content Stream  
Installation and Training  
Optional customization of the Semantic Tagging for your domain  

Case Studies


More Resources

  • Text Analysis for Content Management

    White Papers

    In this white paper, we’ve provided an overview of how we at Ontotext develop and implement Knowledge Graph-based Content Management solutions.

  • Text Analytics for Enterprise Use

    White Papers

    This exclusive Ontotext white paper describes how semantic technologies make any content intelligent and turn it into revenue for your publishing business.

  • What is Text Analysis?


    Text Analysis is about parsing texts in order to extract machine-readable facts from them. The purpose of Text Analysis is to create sets of structured data out of heaps of unstructured, heterogeneous documents.

  • What Is Machine Learning?


    Machine learning (ML) is about teaching computer programs to recognize images, words and sounds by giving them a lot of examples to learn from. Once developed and trained, such algorithms help create systems that can automatically respond to and interpret data.

  • What is Information Extraction?


    Information extraction is the process of extracting specific information from textual sources. It enables the automation of tasks such as smart content classification, integrated search, management and delivery. It also facilitates data-driven activities such as mining for patterns and trends, uncovering hidden relationships, etc.