Recommender utilizes advanced algorithms to analyze content and user behavior to provide personalized recommendations.

Key functionalities and features of Recommender:

• Content Analysis:  Analyzes the content of documents or digital assets to understand their topics, themes, and relationships.
• Matching Semantic Fingerprints of Content: Tracks the tagging applied to individual pieces of content from the controlled vocabularies and suggests content with the same or similar indexing/tagging.
• Recommendation Generation: Based on the content analysis and previous tagging, the system generates individual recommendations for users, suggesting relevant content that matches their interests based on tagging and search hits.
• “More like these” content is suggested based on the user’s current search session and actual data derived from the automatic tagging rather than from search histories, browsing behavior, or content related to other users’ search results.
• Users Click on the article of interest and the system presents similar articles.

Recommender improves content discovery, user engagement, and satisfaction by providing tailored recommendations, enhancing the findability and user experience.