Search Results for "recommendation"
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Showing 21 open source projects for "recommendation"
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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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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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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. ... -
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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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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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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers,... -
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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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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. -
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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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... -
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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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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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Raphaël
JavaScript vector library
...If you want to create your own specific chart or image crop and rotate widget, for example, you can achieve it simply and easily with this library. Raphaël ['ræfeɪəl] uses the SVG W3C Recommendation and VML as a base for creating graphics. This means every graphical object you create is also a DOM object, so you can attach JavaScript event handlers or modify them later. Raphaël’s goal is to provide an adapter that will make drawing vector art compatible cross-browser and easy. Raphaël currently supports Firefox 3.0+, Safari 3.0+, Chrome 5.0+, Opera 9.5+ and Internet Explorer 6.0+. ... -
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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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CSSgram
CSS library for Instagram filters
...We are using pseudo-elements (i.e. :before and :after) to create the filter effects, so you must apply these filters on a containing element (i.e. not a content-block like <img>. The recommendation is to wrap your images in a <figure> tag. If you use custom naming in your CSS architecture, you can add the .scss files for the provided styles within your project and then @extend the filter effects within your style definitions. Mixins allow for multiple filter arguments to be passed into your classes. This is useful for if you want to add filters in addition to the ones provided (i.e. add a blur).
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