Recommendation Engine
Showing 14 of 14 projects
A flexible, fast recommender engine for Python that integrates classic information filtering algorithms with scientific Python packages.
A comparative Python framework for building, evaluating, and deploying multimodal recommender systems with auxiliary data.
Music discovery tool that provides recommendations based on selected Lidarr artists using Last.fm.
A Neo4j-based library for building high-performance recommendation engines with real-time and pre-computed capabilities.
A collaborative filtering recommendation engine library for Go, implementing item-based and user-based approaches.
A C library for product recommendations using collaborative filtering with fast performance and minimal dependencies.
A Ruby SDK for integrating applications with Apache PredictionIO's Event Server and Engine APIs.
An Alexa Skill sample that implements a decision tree algorithm to ask yes/no questions and provide career suggestions.
A JRuby gem that provides Ruby-friendly access to Apache Mahout's scalable machine learning capabilities for recommendations.
An Elasticsearch plugin that integrates with Neo4j to personalize search results using graph data.
A PHP framework for building complex recommendation engines on top of Neo4j graph databases.
A native MySQL plugin for vector storage and similarity search, enabling AI-powered applications directly within your database.
A web application that helps users discover new podcasts through Spotify integration and personalized recommendations.
A demo store template using Shopify Hydrogen with Crossing Minds Beam React for personalized product recommendations.
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