Search Results for "recommendation"
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24 projects for "recommendation" with 2 filters applied:
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Paessler - Monitor Your Whole Network in MinutesWaiting weeks for a monitoring rollout isn't an option when infrastructure doesn't stop running. PRTG's auto-discovery scans your network and suggests from over 200 pre-configured sensor types, so you're watching servers, applications and devices within minutes, not after a multi-week deployment. Enterprise-strength monitoring, without the enterprise complexity. Start your free trial today.
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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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. -
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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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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. -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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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. ... -
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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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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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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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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... -
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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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. ... -
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Similarity
Text similarity calculation Toolkit for Java
...It includes approaches such as edit distance, cosine similarity, Euclidean distance, Jaccard similarity, Jaro distance, Jaro-Winkler distance, Manhattan distance, SimHash with Hamming distance, and Sørensen-Dice coefficient. It also supports Java dependency integration through Maven or Gradle workflows. It is useful for Chinese NLP projects, search features, duplicate detection, recommendation systems, and text analysis experiments. -
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Arxiv Sanity Lite
arxiv-sanity lite: tag arxiv papers of interest get recommendations
Arxiv Sanity Lite is a lightweight rewrite of Karpathy’s original arXiv discovery tool for tracking machine-learning research. It periodically polls the arXiv API for new papers and stores them locally for browsing. Users can tag papers they find relevant and receive recommendations tailored to each tag. Recommendations are generated with support vector machines trained on TF-IDF features extracted from paper abstracts. The web interface supports searching, ranking, sorting, and filtering... -
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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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Swing
A swipeable cards interface
...Swing is lightweight and framework-agnostic, making it easy to integrate into different web projects without heavy dependencies. It is commonly used for building interactive UI components such as recommendation systems or decision-based interfaces. Overall, swing offers a simple yet powerful way to implement gesture-driven interactions in web applications. -
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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. -
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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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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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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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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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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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