Search Results for "recommendation"
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Showing 7 open source projects for "recommendation"
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The All-in-One Commerce Platform for Businesses - ShopifyShopify is a leading all-in-one commerce platform that enables businesses to start, build, and grow their online and physical stores. It offers tools to create customized websites, manage inventory, process payments, and sell across multiple channels including online, in-person, wholesale, and global markets. The platform includes integrated marketing tools, analytics, and customer engagement features to help merchants reach and retain customers. Shopify supports thousands of third-party apps and offers developer-friendly APIs for custom solutions. With world-class checkout technology, Shopify powers over 150 million high-intent shoppers worldwide. Its reliable, scalable infrastructure ensures fast performance and seamless operations at any business size.
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Gorse Recommender System Engine
An open source recommender system service written in Go
An open-source recommender system service written in Go. Recommend items from Popular, latest, user-based, item-based and collaborative filtering. Search the best recommendation model automatically in the background. Support horizontal scaling in the recommendation stage after single node training. Support Redis, MySQL, Postgres, MongoDB, and ClickHouse as its storage backend. Expose RESTful APIs for data CRUD and recommendation requests. Analyze online recommendation performance from recently... -
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Vespa
The open big data serving engine
.... Recommendation, personalization and targeting involves evaluating recommender models over content items to select the best ones. Vespa lets you build applications which does this online, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. This makes it possible to make recommendations specifically for each user or situation, using completely up to date information. -
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NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
NVIDIA Merlin is an open-source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see NVIDIA... -
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JuiceFS
JuiceFS is a distributed POSIX file system built on top of Redis
..., availability, scalability and strong consistency for your data-intensive applications. Purposely built to serve big data scenarios such as self-driving model training, recommendation engine, and Next-generation Gene Sequencing, JuiceFS specializes in high performance and easier management of tens of billion of files management. We bring JuiceFS to developers with the hope that it will be easy to use, reliable, high-performance, and solve all your file storage problems in a cloud environment. -
Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
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Seldon Server
Machine learning platform and recommendation engine on Kubernetes
Seldon Server is a machine learning platform and recommendation engine built on Kubernetes. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. Seldon Core is a progression of the goals of the Seldon-Server project but also a more... -
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easyrec is a recommender system that aims for easy integration of recommendations into web applications. It has a web based admin tool, and its recommendation engine is accessible through a REST API, providing methods like 'other users also bought'
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Design and develop Recommendation and Adaptive Prediction Engines to address eCommerce opportunities. Build a portfolio of engines by creating and porting algorithms from multiple disciplines to a usable form. Try to solve NetFlix and other challenges.
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