Search Results for "recommendation system"

Taste Skill is a project focused on structured skill development, likely combining curated content, learning paths, and interactive elements to help users build expertise in specific domains. It appears to emphasize personalized or modular learning, allowing users to explore topics based on interest or progression level. The platform may integrate recommendation systems or categorized content to guide users through a learning journey efficiently. Its design suggests an emphasis on accessibility and practical skill acquisition rather than purely theoretical knowledge. The repository likely includes tools or interfaces that support content organization, tracking, and user engagement. Overall, it positions itself as a lightweight, adaptable system for continuous learning and skill discovery.

Prastava (hindi for "suggestion") - a generic open source recommendation system built entirely in ruby. This system gives recommendations to users on the basis of their past likings (ratings of items) and an item file(for content based similarity).

This is a recommendation system built in ruby which is able to generate recommendations for user inputted data (a text file and a ratings matrix). It works on a hybrid model of collaborative filtering and content based filtering.