Best AI tools for< Recommendation System Engineer >

Shaped is an AI tool designed to provide relevant recommendations and search results to increase engagement, conversion, and revenue. It offers a configurable system that adapts in real-time, with features such as easy set-up, real-time adaptability, state-of-the-art model library, high customizability, and explainable results. Shaped is suitable for technical teams and offers white-glove support. It specializes in real-time ranking systems and supports multi-modal unstructured data understanding. The tool ensures secure infrastructure and has advantages like increased redemption rate, average order value, and diversity.

Balik Games is a dynamic company specializing in developing innovative games and apps that cater to various interests and needs. With a focus on quality, diversity, and user experience, Balik Games is committed to creating fun, engaging, and easy-to-use products that deliver maximum value to customers. Their app offers a unique combination of ASMR soundscapes and a personalized recommendation system powered by AI, providing a customized experience tailored to individual preferences. Two of their popular games include Blocks!, a puzzle game challenging problem-solving skills, and Swipe Kingdom, a card-based game where players take on the role of a Viking King.

Tech Xplore is a leading source of science and technology news, covering the latest breakthroughs in research and innovation across a wide range of disciplines, including artificial intelligence, robotics, computer science, and more. The website provides in-depth articles, interviews with experts, and up-to-date information on the latest developments in the field of AI and its applications.

NVIDIA is a world leader in artificial intelligence computing. The company's products and services are used by businesses and governments around the world to develop and deploy AI applications. NVIDIA's AI platform includes hardware, software, and tools that make it easy to build and train AI models. The company also offers a range of cloud-based AI services that make it easy to deploy and manage AI applications. NVIDIA's AI platform is used in a wide variety of industries, including healthcare, manufacturing, retail, and transportation. The company's AI technology is helping to improve the efficiency and accuracy of a wide range of tasks, from medical diagnosis to product design.

Amazon Science is a research and development organization within Amazon that focuses on developing new technologies and products in the fields of artificial intelligence, machine learning, and computer science. The organization is home to a team of world-renowned scientists and engineers who are working on a wide range of projects, including developing new algorithms for machine learning, building new computer vision systems, and creating new natural language processing tools. Amazon Science is also responsible for developing new products and services that use these technologies, such as the Amazon Echo and the Amazon Fire TV.

Big Vision provides consulting services in AI, computer vision, and deep learning. They help businesses build specific AI-driven solutions, create intelligent processes, and establish best practices to reduce human effort and enable faster decision-making. Their enterprise-grade solutions are currently serving millions of requests every month, especially in critical production environments.

Webb.ai is an AI-powered platform that offers automated troubleshooting for Kubernetes. It is designed to assist users in identifying and resolving issues within their Kubernetes environment efficiently. By leveraging AI technology, Webb.ai provides insights and recommendations to streamline the troubleshooting process, ultimately improving system reliability and performance. The platform is user-friendly and caters to both beginners and experienced users in the field of Kubernetes management.

AWS Docs GPT is an AI-powered search and chat tool designed specifically for AWS Documentation. It utilizes the power of artificial intelligence to enhance the user experience by providing accurate search results and interactive chat support. With Antimetal integration, users can optimize their AWS costs by up to 75% through AI-driven recommendations. The tool aims to streamline the process of navigating and understanding AWS documentation, making it easier for users to find relevant information and troubleshoot issues effectively.

Shaped is a cloud-based platform that provides APIs and tools for building and deploying ranking systems. It offers a variety of features to help developers quickly and easily create and manage ranking models, including a multi-connector SQL interface, a real-time feature store, and a library of pre-built models. Shaped is designed to be scalable, cost-efficient, and easy to use, making it a great option for businesses of all sizes.

Wasps is an AI code review tool that integrates seamlessly into VSCode, providing developers with a fast and efficient way to understand their codebase, detect and fix code issues using AI and Gitsecure. With Wasps, developers can identify and fix buggy & vulnerable code in minutes, receive clear and actionable feedback driven by deep analysis, and get recommendations for potential issues and improvements within their codebase. The tool allows developers to keep coding as usual while Wasps analyzes their code for them, making it easier to maintain code quality and keep bugs out of their code.

ICD AI is an advanced artificial intelligence tool designed to assist healthcare professionals in accurately assigning diagnostic codes to patient records. The tool utilizes machine learning algorithms to analyze medical data and suggest appropriate ICD codes, streamlining the coding process and reducing errors. With its user-friendly interface and robust features, ICD AI is revolutionizing medical coding practices and improving efficiency in healthcare facilities.

JobsRemote.ai is a free artificial intelligence-based platform that offers a curated selection of remote job-friendly tools to enhance the remote work experience. Users can browse thousands of handpicked tools suitable for remote jobs, ensuring high-quality recommendations without ads, scams, or junk listings. The platform focuses on providing legitimate and suitable job listings from top companies worldwide, streamlining the application process for job seekers. With a user-friendly interface and personalized recommendations, JobsRemote.ai aims to connect remote professionals with quality remote job opportunities efficiently.

Linkter is the #1 AI internal linking tool designed for SEO superstars. It automates the process of building strategic internal links, saving SEOs hundreds of hours of manual work. With advanced features like an AI recommendation system, anchor text manager, and custom AI implementation, Linkter enhances SEO rankings, indexation, and user experience. The tool revolutionizes website optimization by streamlining internal linking tasks and improving overall website visibility and performance.

Critique is an AI tool that redefines browsing by offering autonomous fact-checking, informed question answering, and a localized universal recommendation system. It automatically critiques comments and posts on platforms like Reddit, Youtube, and Linkedin by vetting text on any website. The tool cross-references and analyzes articles in real-time, providing vetted and summarized information directly in the user's browser.

Swipe Insight is a mobile application that provides users with daily updates on digital marketing and analytics trends, news, and strategies. The app features a personalized feed that adapts to the user's preferences, intelligent insights that distill complex topics into concise summaries, and a curated selection of content from over 100 trusted sources. Swipe Insight is designed to help users stay ahead in the industry with just minutes of reading per day.

Gift Recommender is an AI-powered application designed to assist users in finding the perfect gift for their loved ones. By providing basic information about the recipient such as name, age, gender, price range, and interests, the AI generates personalized gift recommendations. The system learns from user feedback to continuously improve its suggestions. While the AI provides recommendations, it acknowledges that the best gift is often something personal and encourages users to provide feedback for better training.

Chat Whisperer is a sophisticated chat creator that relies on artificial intelligence to learn from customized data. This allows users to design exceptional and highly tailored chatting experiences. Businesses can deliver specific customer service, teachers can create interactive learning bots, and individuals may use the platform to entertain themselves through building personal chatbots.

MedoSync is an AI-driven health platform that empowers users to monitor and analyze their vital and medical data, leveraging AI to provide personalized insights and recommendations for a healthier life. Users can upload lab results, digitize medical documents, use an AI symptom checker, create accounts for family members, and integrate with their healthcare system. The platform offers easy data export, accuracy in health insights, and personalized health recommendations, with a high user satisfaction rate.

Yonder is an AI-powered chatbot and review platform designed specifically for the tourism industry. It helps tourism businesses to increase website sales, generate more enquiries, and save staff time by providing AI chatbot services, personalized recommendations, live reviews showcase, website chat support, customer surveys, team feedback categorization, and online review management. Yonder's AI chatbot can answer up to 80% of customer questions immediately on the website and Messenger, 24/7. The platform also offers features like conversation analytics, ratings breakdown, and integration with reservation systems for automation and efficiency.

Bonfire is a custom AI chatbot platform that offers personalized concierge experiences for users. It allows companies to build enterprise-grade chatbots trained on their unique datasets, enhancing customer interactions and user engagement rates. The platform supports over 100 languages and offers features such as personalized product recommendations, lead scoring, file attachments, and customized user journeys. Bonfire replicates human conversation through its Adaptive Learning Technology, requiring no coding for integration. The platform securely stores data in a cloud-based system and allows integration of various structured and unstructured datasets.

Recommenders is a project under the Linux Foundation of AI and Data that assists researchers, developers, and enthusiasts in prototyping, experimenting with, and bringing to production a range of classic and state-of-the-art recommendation systems. The repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It covers tasks such as preparing data, building models using various recommendation algorithms, evaluating algorithms, tuning hyperparameters, and operationalizing models in a production environment on Azure. The project provides utilities to support common tasks like loading datasets, evaluating model outputs, and splitting training/test data. It includes implementations of state-of-the-art algorithms for self-study and customization in applications.

This paper list is about Large Language Model-enhanced Recommender System. It also contains some related works. Keywords: recommendation system, large language models

This repository is a collection of papers and resources related to recommendation systems, focusing on foundation models, transferable recommender systems, large language models, and multimodal recommender systems. It explores questions such as the necessity of ID embeddings, the shift from matching to generating paradigms, and the future of multimodal recommender systems. The papers cover various aspects of recommendation systems, including pretraining, user representation, dataset benchmarks, and evaluation methods. The repository aims to provide insights and advancements in the field of recommendation systems through literature reviews, surveys, and empirical studies.

A curated repository of code recipes, demos, and resources for basic and advanced Redis use cases in the AI ecosystem. It includes demos for ArxivChatGuru, Redis VSS, Vertex AI & Redis, Agentic RAG, ArXiv Search, and Product Search. Recipes cover topics like Getting started with RAG, Semantic Cache, Advanced RAG, and Recommendation systems. The repository also provides integrations/tools like RedisVL, AWS Bedrock, LangChain Python, LangChain JS, LlamaIndex, Semantic Kernel, RelevanceAI, and DocArray. Additional content includes blog posts, talks, reviews, and documentation related to Vector Similarity Search, AI-Powered Document Search, Vector Databases, Real-Time Product Recommendations, and more. Benchmarks compare Redis against other Vector Databases and ANN benchmarks. Documentation includes QuickStart guides, official literature for Vector Similarity Search, Redis-py client library docs, Redis Stack documentation, and Redis client list.

Colossal-AI is a deep learning system for large-scale parallel training. It provides a unified interface to scale sequential code of model training to distributed environments. Colossal-AI supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer.

Mobius is an AI infra platform including realtime computing and training. It is built on Ray, a distributed computing framework, and provides a number of features that make it well-suited for online machine learning tasks. These features include: * **Cross Language**: Mobius can run in multiple languages (only Python and Java are supported currently) with high efficiency. You can implement your operator in different languages and run them in one job. * **Single Node Failover**: Mobius has a special failover mechanism that only needs to rollback the failed node itself, in most cases, to recover the job. This is a huge benefit if your job is sensitive about failure recovery time. * **AutoScaling**: Mobius can generate a new graph with different configurations in runtime without stopping the job. * **Fusion Training**: Mobius can combine TensorFlow/Pytorch and streaming, then building an e2e online machine learning pipeline. Mobius is still under development, but it has already been used to power a number of real-world applications, including: * A real-time recommendation system for a major e-commerce company * A fraud detection system for a large financial institution * A personalized news feed for a major news organization If you are interested in using Mobius for your own online machine learning projects, you can find more information in the documentation.

LLMRec is a PyTorch implementation for the WSDM 2024 paper 'Large Language Models with Graph Augmentation for Recommendation'. It is a novel framework that enhances recommenders by applying LLM-based graph augmentation strategies to recommendation systems. The tool aims to make the most of content within online platforms to augment interaction graphs by reinforcing u-i interactive edges, enhancing item node attributes, and conducting user node profiling from a natural language perspective.

NineRec is a benchmark dataset suite for evaluating transferable recommendation models. It provides datasets for pre-training and transfer learning in recommender systems, focusing on multimodal and foundation model tasks. The dataset includes user-item interactions, item texts in multiple languages, item URLs, and raw images. Researchers can use NineRec to develop more effective and efficient methods for pre-training recommendation models beyond end-to-end training. The dataset is accompanied by code for dataset preparation, training, and testing in PyTorch environment.

The Next-Generation LLM-based Recommender Systems Survey is a comprehensive overview of the latest advancements in recommender systems leveraging Large Language Models (LLMs). The survey covers various paradigms, approaches, and applications of LLMs in recommendation tasks, including generative and non-generative models, multimodal recommendations, personalized explanations, and industrial deployment. It discusses the comparison with existing surveys, different paradigms, and specific works in the field. The survey also addresses challenges and future directions in the domain of LLM-based recommender systems.

Vespa is an open-source search and AI engine that provides a unified platform for building and deploying search and AI applications. Vespa sample applications showcase various use cases and features of Vespa, including basic search, recommendation, semantic search, image search, text ranking, e-commerce search, question answering, search-as-you-type, and ML inference serving.

Deej-A.I. is an advanced machine learning project that aims to revolutionize music recommendation systems by using artificial intelligence to analyze and recommend songs based on their content and characteristics. The project involves scraping playlists from Spotify, creating embeddings of songs, training neural networks to analyze spectrograms, and generating recommendations based on similarities in music features. Deej-A.I. offers a unique approach to music curation, focusing on the 'what' rather than the 'how' of DJing, and providing users with personalized and creative music suggestions.

KDB.AI is a time-based vector database that allows developers to build scalable, reliable, and real-time applications by providing advanced search, recommendation, and personalization for Generative AI applications. It supports multiple index types, distance metrics, top-N and metadata filtered retrieval, as well as Python and REST interfaces. The repository contains samples demonstrating various use-cases such as temporal similarity search, document search, image search, recommendation systems, sentiment analysis, and more. KDB.AI integrates with platforms like ChatGPT, Langchain, and LlamaIndex. The setup steps require Unix terminal, Python 3.8+, and pip installed. Users can install necessary Python packages and run Jupyter notebooks to interact with the samples.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

Taipy is an open-source Python library for easy, end-to-end application development, featuring what-if analyses, smart pipeline execution, built-in scheduling, and deployment tools.

2025-AI-College-Jobs is a repository containing a comprehensive list of AI/ML & Data Science jobs suitable for college students seeking internships or new graduate positions. The repository is regularly updated with positions posted within the last 120 days, featuring opportunities from various companies in the USA and internationally. The list includes positions in areas such as research scientist internships, quantitative research analyst roles, and other data science-related positions. The repository aims to provide a valuable resource for students looking to kickstart their careers in the field of artificial intelligence and machine learning.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

CoLLM is a novel method that integrates collaborative information into Large Language Models (LLMs) for recommendation. It converts recommendation data into language prompts, encodes them with both textual and collaborative information, and uses a two-step tuning method to train the model. The method incorporates user/item ID fields in prompts and employs a conventional collaborative model to generate user/item representations. CoLLM is built upon MiniGPT-4 and utilizes pretrained Vicuna weights for training.

LearnOpenCV is a repository containing code for Computer Vision, Deep learning, and AI research articles shared on the blog LearnOpenCV.com. It serves as a resource for individuals looking to enhance their expertise in AI through various courses offered by OpenCV. The repository includes a wide range of topics such as image inpainting, instance segmentation, robotics, deep learning models, and more, providing practical implementations and code examples for readers to explore and learn from.

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

Recommends self-hosted IT solutions, tailored for professionals (from https://awesome-selfhosted.net/)

Expert in residential speaker systems, offering detailed advice and product recommendations.

Interact with the HVAC matrix

Red Team Recipe and Guide for Fun & Profit.

Given a set of Parameters, Provides a set of Order Recommendations

A movie and TV show , Songs & Books recommendation assistant for various streaming platforms.

나에게 맞는 직업과 진로 추천받기. 적성에 맞는 진로 상담, 직업추천, 휴먼디자인, Based on Human Design System

Focused solely on tech in the legal system. No unrelated advice or recommendations.

意思決定が必要な時に、３基のスーパーコンピュータが多数決で決めてくれます

Expert in macroeconomics, providing holistic and system-wide insights.

Recomenda programas para fluxogramas e ilustra visualmente.

Ethics-focused GPT builder assistant based on European AI guidelines, recommendations and regulations

A counselor aiding in writing recommendation letters for PhD applications, with a formal and informative tone.

Expert in drafting recommendation letters for U.S. green card application under EB-1A and EB2-NIW applications. Please start from the conversation starters but with the info of yourself.

为您找到想要的酒店，请说您的要求Finds hotels in China with preferd demand

Ask me to recommend a plugin or script from the official Hex-Rays plugin repository

Finds the best Shopify app for your requirements and budget

The best non-fiction book recommendation assistant.

Expert on Twitter's recommendation algorithm with code insights.