Search Results for "recommendation"
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Showing 48 open source projects for "recommendation"
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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RecBole
A unified, comprehensive and efficient recommendation library
A unified, comprehensive and efficient recommendation library. We design general and extensible data structures to unify the formatting and usage of various recommendation datasets. We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. -
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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 inserted feedback. ... -
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Recommenders 2023
Best Practices on Recommendation Systems
Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems. Recommenders is a project under the Linux Foundation of AI and Data. -
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DeepMatch
A deep matching model library for recommendations & advertising
DeepMatch is an open-source deep matching library built for recommendation and advertising systems. It helps developers train models that learn vector representations for users and items. These representations can be exported and used in approximate nearest neighbor search for large-scale retrieval. The library supports familiar Keras workflows through model.fit() and model.predict(). Its model collection includes FM, DSSM, YouTubeDNN, NCF, SDM, MIND, and ComiRec. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Agent Executor (AX)
Google's open source distributed agent runtime
Agent Executor (AX) is a Google research project for learning discrete choice models through differentiable maximum likelihood estimation. It is designed for situations where a model needs to predict choices from a finite set of alternatives, such as ranking, recommendation, preference modeling, or decision behavior analysis. The project provides JAX-based tools for defining and training choice models with automatic differentiation. It focuses on flexible model construction rather than a single fixed estimator, making it useful for researchers who want to experiment with different utility functions and optimization setups. ax is especially relevant for machine learning and econometrics workflows that need scalable, differentiable approaches to choice modeling. ... -
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Vespa
The open big data serving engine
...This makes it easy to create high-performing search applications at any scale, whether you want to use traditional techniques or a modern vector-based approach. You can even combine both approaches efficiently in the same query, something no other engine can do. 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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X For You Feed Algorithm
Algorithm powering the For You feed on X
X For You Feed Algorithm is the open-sourced core recommendation system that powers the For You feed on X (the social network formerly known as Twitter), and it represents one of the first times a major social platform has published production-level ranking code for public review and experimentation. The repository contains the full pipeline that ingests user engagement and content candidate data, processes it through retrieval, hydration, filtering, scoring, and selection layers, and ultimately ranks posts to show what appears in a user’s feed. ... -
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NoSignups
A list of tools that are open-source, in-browser
...Its website is built with React and TypeScript and organizes tools across productivity, development, design, writing, privacy, media, education, and other categories. Entries include descriptions, searchable tags, source links, licenses, star counts, and recommendation metadata when available. Featured listings highlight unusual or particularly useful tools within the growing catalog. The repository also accepts community submissions, provided the suggested software works without mandatory registration and fits the project's privacy-oriented philosophy. -
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HowToLiveBetter
Evidence-based living guidelines sorted by cost-effectiveness
...It covers health, longevity, emergency response, personal finance, legal risks, unemployment, entrepreneurship, relationships, parenting, travel, and skill development. Each recommendation explains what resources it costs and what outcome it is intended to improve. Evidence ratings are tied to journal research and official documents rather than general web commentary. The web interface lets readers search and filter recommendations by dimensions such as money, time, and required effort. The repository also includes longer essays, reference material, verification notes, and structured book chapters. ... -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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DCV Color Primitives
DCV Color Primitives Library
DCV Color Primitives is a library to perform image color model conversion. Aware of the underlying hardware and supplemental cpu extension sets (up to avx2). Support data coming from a single buffer or coming from multiple image planes. Support non-tightly packed data. Support images greater than 4GB (64 bit). Convert an image from bgra to nv12 (single plane) format containing yuv in BT601. You might want to propagate errors to the caller function or mix with some other error types. So far,... -
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Thulite
Web framework designed for speed, security, and SEO
Thulite is an AI-powered search and recommendation engine that enhances search functionality in applications. It provides intelligent query processing, result ranking, and personalized recommendations. -
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AWS Node Termination Handler
Gracefully handle EC2 instance shutdown within Kubernetes
This project ensures that the Kubernetes control plane responds appropriately to events that can cause your EC2 instance to become unavailable, such as EC2 maintenance events, EC2 Spot interruptions, ASG Scale-In, ASG AZ Rebalance, and EC2 Instance Termination via the API or Console. If not handled, your application code may not stop gracefully, take longer to recover full availability, or accidentally schedule work to nodes that are going down. Secure and resizable compute capacity for... -
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how-to-live-better
Online single-page reading version of the 528-item "High-Value Life"
How to Live Better is a self-contained web reader for the open-source High Cost-Performance Life Guide. It renders all 32 sections and 528 recommendations from the upstream project into a single searchable HTML page. Each recommendation is presented as a card with evidence grade, value tier, and cost labels. Users can search the full guide, filter for top-grade evidence, or show only simplified explanations. A synchronized table of contents, responsive mobile layout, dark mode, and print support improve navigation. The page uses no external resources, so it can be opened and read entirely offline. ... -
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Goldilocks
Get your resource requests "Just Right"
Goldilocks is a utility that can help you identify a starting point for resource requests and limits. By using the Kubernetes vertical-pod-autoscaler in recommendation mode, we can see a suggestion for resource requests on each of our apps. This tool creates a VPA for each workload in a namespace and then queries them for information. Once your VPAs are in place, you'll see recommendations appear in the Goldilocks dashboard. -
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Node.js CLI Apps Best Practices
The largest Node.js CLI Apps best practices list
...The guide emphasizes POSIX conventions, helpful prompts, structured output, predictable configuration, meaningful exit codes, and clean separation of stdout and stderr. It also discusses dependency size, shell completion, containerization, debugging, and cross-platform behavior. Each recommendation explains why the practice matters and often links to practical tools or example projects. Multiple translations are available, and an AI-agent-ready skill file makes the material reusable inside coding assistants. -
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Errbit
The open source error catcher that's Airbrake API compliant
...Notices with identical fingerprints appear in the UI as different occurences of the same error and notices with differing fingerprints are displayed as separate errors. Following the recommendation of 12factor.net, Errbit takes all of its configuration from environment variables. You can use dotenv, which is included in the Gemfile, to fill in any values that you can't or won't supply through the environment. -
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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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JuiceFS
JuiceFS is a distributed POSIX file system built on top of Redis
...Whether it's a public cloud, private cloud, or hybrid cloud, JuiceFS is available on any cloud of your choice and delivers flexibility, 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. -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
...Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). The general recommendation is to use suitable augs for your data and as many as possible, then after some time of training disable the most destructive (for image) augs. You can turn on automatic mixed precision with one flag --amp. You should expect it to be 33% faster and save up to 40% memory. Aim is an open-source experiment tracker that logs your training runs, and enables a beautiful UI to compare them. -
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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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Digraph3
A collection of python3 modules for Algorithmic Decision Theory
...There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs. -
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applied-ml
Papers & tech blogs by companies sharing their work on data science
The applied-ml repository is a rich, curated collection of papers, technical articles, and case-study blog posts about how machine learning (ML) and data-driven systems are applied in real production environments by major companies. Instead of focusing solely on theoretical ML research, this repo highlights industry-scale challenges: data collection, quality, infrastructure, feature stores, model serving, monitoring, scalability, and how ML is embedded in product workflows. It acts as a... -
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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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AlgoNotes
Summary of articles from the official account of Qianmeng Study Notes
AlgoNotes is a curated technical knowledge repository focused on recommendation systems, search, advertising, and machine learning. It collects articles, engineering practices, research summaries, and interview materials in one organized index. Major sections cover ranking and conversion prediction, recall and matching, user profiles, and feature engineering. It also includes recommendation and search topics, computational advertising, big data, and graph algorithms. -
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ml-surveys
Survey papers summarizing advances in deep learning, NLP, CV, graphs
The ml-surveys repository is a broad, maintainable overview of survey papers across many subfields of machine learning — including deep learning, NLP, computer vision, graph ML, reinforcement learning, recommendation systems, embeddings, meta-learning, and more. Instead of diving into code or experiments, this repo gathers authoritative survey and review articles, summarizing the state-of-the-art, trends, challenges, and directions within each subdomain. For someone trying to get up to speed with a new ML subfield — say graph neural networks or meta-learning — ml-surveys offers a curated reading list of foundational and recent works, helping map the landscape quickly. ...
