Search Results for "recommendation"
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25 projects for "recommendation" with 2 filters applied:
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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Transformers4Rec
Transformers4Rec is a flexible and efficient library
...Those challenges are addressed by the sequential recommendation task. -
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DLRM
An implementation of a deep learning recommendation model (DLRM)
DLRM (Deep Learning Recommendation Model) is Meta’s open-source reference implementation for large-scale recommendation systems built to handle extremely high-dimensional sparse features and embedding tables. The architecture combines dense (MLP) and sparse (embedding) branches, then interacts features via dot product or feature interactions before passing through further dense layers to predict click-through, ranking scores, or conversion probabilities. -
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Job Recommend
The basics of building a job recommendation workflow
...The code encourages experimentation, inviting you to swap scoring rules, adjust weights, or plug in alternative representations. It serves as a starting point for understanding recommendation pipelines before moving to production-grade systems. -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs. The repository includes quizzes, solutions, and instructor materials to make the content usable in classrooms or self-study. It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. ... -
Simple, Secure Domain RegistrationRegister or renew your domain and pay only what we pay. No markups, hidden fees, or surprise add-ons. Choose from over 400 TLDs (.com, .ai, .dev). Every domain is integrated with Cloudflare's industry-leading DNS, CDN, and free SSL to make your site faster and more secure. Simple, secure, at-cost domain registration.
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Node.js Best Practices
The Node.js best practices list (July 2024)
...It organizes advice into clear sections—project structure, error handling, security, testing, performance, reliability, and maintainability—so teams can adopt improvements incrementally. Each guideline is phrased as a practical recommendation with motivation and trade-offs, not just a rule, which makes it easier to reach team consensus. The content aims to be technology-agnostic within the Node ecosystem, providing patterns that work whether you’re using Express, Fastify, Nest, or custom frameworks. It is designed for real-world constraints: production deployment, observability, CI/CD, containerization, and cross-functional collaboration. ... -
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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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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
...Its training loop is built for throughput: asynchronous I/O, memory-mapped tensors, and lock-free updates keep GPUs and CPUs fed even at extreme scale. The toolkit includes evaluation metrics and export tools so learned embeddings can be used in downstream nearest-neighbor search, recommendation, or analytics. In practice, PBG’s design lets practitioners train high-quality graph embeddings. -
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Functional, Data Science Intro To Python
[tutorial]A functional, Data Science focused introduction to Python
...The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning, Linear Optimization, build systems, command line tools, recommendation engines, Sentiment Analysis and Cloud Computing. -
Enterprise and Small Business CRM Solution | Clear C2 C2CRMC2CRM consists of four modules that integrate to provide a comprehensive CRM solution: Relationship Management, Sales Automation, Marketing Automation, and Customer Service. Only buy what each user needs.
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LibRec
Leading Java Library for Recommender Systems
LibRec is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: rating prediction and item ranking. -
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MMR Decoder
A decoder for the ITU-T Recommendation T.6 coding scheme
"MMR Decoder" is a C++ decoder for the ITU-T Recommendation T.6 coding scheme. -
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TiVo style recommendation engine for MythTV. MythMagic selects and automatically records shows from your program guide based on previous viewing habits. Recordings can be accepted or rejected to improve recommendation accuracy.
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This project has the objective of defining a recommendation layer that will communicate with several LMS like .LRN, Moodle, etc.
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The project consist in an open source implementation of the W3C Recommendation of the XML Key Management Specification 2.0 XKMS 2.0. It is compound of a XKMS Server and a Client API to access to the Server via Web Service.
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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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An implementation (parser/interpreter) of SPARQL, a query language for RDF data that is the current recommendation of the W3C. Allows easy cusomization of the language and plugs in to several popular RDF servers, like Sesame.
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Define 2nd generation Bittorrent protocol with social networking, recommendation, tag-based navigation, distributed moderation, and real-time streaming. Remove .torrent, tracker, and website from architecture. Create reference implement. with ABC project
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This Perl module allows you to validate XML documents against a W3C XML Schema. This module does not implement the full W3C XML Schema recommendation, but a useful subset.
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JBind is a data binding framework linking Java and XML. The framework consists of a schema compiler for generating Java sources and a runtime environment. JBind supports most of the W3C XML schema recommendation of 2 May 2001.
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EPG downloader of S k y p e r f e c T V. Download EPG to local and send recommendation list to users via email. Written in Java.
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Processor Interface for XML Inclusions (PIXI) is an implementation of the W3C XInclude Recommendation in Java.
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XML::XSLT is a collection of Perl modules that perform XSL transforms on XML documents, fully implementing the W3C XSLT recommendation. It is based upon the Apache Group's project Xalan and Keith Visco's work on XSL:P, both of which are in Java.
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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.
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fashion-clip
CLIP model fine-tuned for zero-shot fashion product classification
...FashionCLIP 2.0, the latest version, uses the laion/CLIP-ViT-B-32-laion2B-s34B-b79K checkpoint for improved accuracy, achieving better F1 scores across multiple benchmarks compared to earlier versions. It supports multilingual fashion queries and works best with clean, product-style images against white backgrounds. The model can be used for product search, recommendation systems, or visual tagging in e-commerce platforms.
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