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
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 Solution |
Custom Solution |
|---|---|---|
| Contextual Recommendation |  |
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| Behavioural Recommendation | |
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| Hybrid Algorithms | |
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| Out of the Box Semantic Tagging | |
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| Integration with Click/Content Stream | |
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| Installation and Training | |
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| Support | |
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| Optional customization of the Semantic Tagging for your domain | |
Case Studies
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Increasing User Engagement in Publishing Platforms by Interlinking Ad Serving with Semantic Technology
Ontotext’s solution for smarter recommendations and ad serving enables a leading media publisher to know more about their content as well as their readers, improve its recommendation system, boost user engagement and adopt a sophisticated ad serving system that targets a more segmented audience.
More Resources
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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.
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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.
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What is Text Analysis?
Fundamentals
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.
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What Is Machine Learning?
Fundamentals
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.
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What is Information Extraction?
Fundamentals
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.
