Best AI tools for< Recommender Systems Engineer >

Victor Dibia's website showcases his expertise in Applied Machine Learning and Human-Computer Interaction (HCI). He is a Principal Research Software Engineer at Microsoft Research, focusing on Generative AI. The site features his publications, projects, CV, and blog posts, covering topics such as multi-agent systems, recommender systems, and more. Victor's work has been recognized in conferences and media outlets, highlighting his contributions to the field of AI and HCI.

Superlinked is a compute framework for your information retrieval and feature engineering systems, focused on turning complex data into vector embeddings. Vectors power most of what you already do online - hailing a cab, finding a funny video, getting a date, scrolling through a feed or paying with a tap. And yet, building production systems powered by vectors is still too hard! Our goal is to help enterprises put vectors at the center of their data & compute infrastructure, to build smarter and more reliable software.

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.

PC Builder AI is an artificial intelligence tool developed by AE Studio to assist users in building a customized PC based on their budget and preferences. The tool allows users to input their desired budget and PC specifications, and it provides real-time price updates to help users get the best value for their money. With a user-friendly interface, PC Builder AI simplifies the process of selecting components and building a PC tailored to individual needs. Whether you are a tech enthusiast or a novice, PC Builder AI can help you create a high-performance PC without the hassle of researching and comparing components.

OtterTune was a database tuning service start-up founded by Carnegie Mellon University. Unfortunately, the company is no longer operational. The founder, DJ OT, is currently in prison for a parole violation. Despite its closure, OtterTune was known for its innovative approach to database tuning. The website now serves as a research archive and provides access to its GitHub repository.

MiMi is a website intelligence tool that uses AI to enhance the user experience and drive sales. It offers a range of features including semantic search, chatbot, recommendations, virtual assistant, dynamic pricing, and automation. MiMi's AI engine can automatically learn and update knowledge from your site to provide an AI chatbot that can answer questions from visitors automatically. The machine learning algorithms can also learn from your site products and visitor behavior to bring recommender systems for your site. MiMi's AI algorithm serves as a virtual sales assistant, assisting websites in making flexible and tailored pricing decisions for each customer based on their behavior.

Gnod is a global network of discovery that uses artificial intelligence to help users discover new things they might like. It offers a variety of projects, including music, art, literature, and movies. Gnod also has a search engine comparison tool that allows users to select an engine every time they search.

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.

5-Out is an AI Restaurant Forecasting Software designed to boost profit for restaurants. It utilizes machine learning, artificial intelligence, and predictive analysis to automate smarter decisions. The application integrates various systems like Point of Sale, labor scheduling, purchasing, inventory, weather, social media, and more to predict sales and optimize labor and purchasing. With real-time insights and recommendations, 5-Out helps restaurants make data-driven decisions to increase profitability.

The website ai.prodi.gg encountered an Origin DNS error, which is a common issue related to the domain name system. The error message indicates that the Cloudflare network is currently unable to resolve the requested domain. Visitors are advised to try again in a few minutes, while website owners are recommended to check their DNS settings, especially if using a CNAME origin record. The page also provides additional troubleshooting information for further assistance.

Inventoro is a smart inventory forecasting and replenishment tool that helps businesses optimize their inventory management processes. By analyzing past sales data, the tool predicts future sales, recommends order quantities, reduces inventory size, identifies profitable inventory items, and ensures customer satisfaction by avoiding stockouts. Inventoro offers features such as sales forecasting, product segmentation, replenishment, system integration, and forecast automations. The tool is designed to help businesses decrease inventory, increase revenue, save time, and improve product availability. It is suitable for businesses of all sizes and industries looking to streamline their inventory management operations.

Suki Assistant is an enterprise-grade AI assistant designed to help clinicians save time by providing ambient documentation, dictation, ICD-10 and HCC coding, and answering questions in one solution. It offers deep EHR integrations with all major EHRs, ensuring safe AI practices, hassle-free partnership, proven ROI, and advanced EHR integrations. Suki is trusted by health systems across the country for its reliability, scalability, and convenience in clinical documentation.

Vexisai.com is a website that serves as a domain parking page created by the domain owner using Sedo Domain Parking. It provides resources and information related to the domain. The webpage does not have any specific AI tool or application functionality but rather acts as a placeholder for the domain. Please note that Sedo, the service used for domain parking, is not affiliated with any third-party advertisers and does not endorse or recommend any specific services or trademarks.

VideaHealth is a dental AI platform trusted by dentists and DSOs. It enhances diagnostics and streamlines workflows using clinical AI to identify and convert treatments across major oral conditions. The platform combines practice management system data with AI insights to elevate patient care and empower dental practices. VideaHealth offers advanced FDA-cleared detection algorithms to detect suspect diseases, provides AI-powered insights for data-driven decisions, and delivers real-time chairside assistance to dentists.

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.

Finta is an AI-powered platform designed to streamline fundraising processes for startups and high-growth companies. Trusted by over 8,000 top-tier companies, funds, and partners, Finta offers tools to optimize fundraising potential, enhance investor relations, and provide real-time insights. The platform leverages AI technology to personalize email scripts, recommend top investors, and facilitate warm introductions. Finta also features a comprehensive CRM system, due diligence smart links, and a collaborative environment for team members, shareholders, and advisors. With a focus on empowering users with tools and knowledge, Finta aims to revolutionize the fundraising experience.

CloseFactor is an AI-powered GTM Operating System that helps sales teams close more deals by providing tailored opportunities with potential buyers who are ready to make a purchase. The platform utilizes machine learning and data analysis to identify the best-fit accounts, research their buying readiness in real-time, recommend contact engagements based on past successful deals, and automatically generate customized messages using generative AI. CloseFactor also assists in identifying the Ideal Customer Profile (ICP) by analyzing common characteristics of closed-won deals, enabling users to target the right accounts effectively. The tool has shown to increase the quality and quantity of pipeline from target accounts, improve marketing spend optimization, and enhance personalized outreach for better sales outcomes.

The Run Recommender is a web-based tool that helps runners find the perfect pair of running shoes. It uses a smart algorithm to suggest options based on your input, giving you a starting point in your search for the perfect pair. The Run Recommender is designed to be user-friendly and easy to use. Simply input your shoe width, age, weight, and other details, and the Run Recommender will generate a list of potential shoes that might suit your running style and body. You can also provide information about your running experience, distance, and frequency, and the Run Recommender will use this information to further refine its suggestions. Once you have a list of potential shoes, you can click on each shoe to learn more about it, including its features, benefits, and price. You can also search for the shoe on Amazon to find the best deals.

The Movie & Book Recommender is an AI-powered tool that helps users discover their next favorite movie or book by providing personalized recommendations. Users can choose between a movie recommender and a book recommender, with options to receive 2, 4, 5, 6, 8, or 10 recommendations. The tool was built by Dapo Adedire and utilizes AI technology from OpenAI, with a user-friendly interface for seamless navigation and exploration of recommendations.

The One-Stop Natural Language Hotel Recommender is an AI-powered tool that simplifies the process of finding the perfect hotel for your needs. By utilizing natural language processing technology, the tool can understand your preferences and requirements, and provide you with personalized hotel recommendations. It considers factors such as proximity to popular places, top-rated establishments, summarized reviews, and staying within your budget. With this tool, you can easily find the ideal accommodation for your next trip without the hassle of extensive research.

RecAI is a project that explores the integration of Large Language Models (LLMs) into recommender systems, addressing the challenges of interactivity, explainability, and controllability. It aims to bridge the gap between general-purpose LLMs and domain-specific recommender systems, providing a holistic perspective on the practical requirements of LLM4Rec. The project investigates various techniques, including Recommender AI agents, selective knowledge injection, fine-tuning language models, evaluation, and LLMs as model explainers, to create more sophisticated, interactive, and user-centric recommender systems.

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.

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.

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.

Metaflow is a user-friendly library designed to assist scientists and engineers in developing and managing real-world data science projects. Initially created at Netflix, Metaflow aimed to enhance the productivity of data scientists working on diverse projects ranging from traditional statistics to cutting-edge deep learning. For further information, refer to Metaflow's website and documentation.

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

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.

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.

MindsDB is a platform for customizing AI from enterprise data. You can create, serve, and fine-tune models in real-time from your database, vector store, and application data. MindsDB "enhances" SQL syntax with AI capabilities to make it accessible for developers worldwide. With MindsDB’s nearly 200 integrations, any developer can create AI customized for their purpose, faster and more securely. Their AI systems will constantly improve themselves — using companies’ own data, in real-time.

ABigSurveyOfLLMs is a repository that compiles surveys on Large Language Models (LLMs) to provide a comprehensive overview of the field. It includes surveys on various aspects of LLMs such as transformers, alignment, prompt learning, data management, evaluation, societal issues, safety, misinformation, attributes of LLMs, efficient LLMs, learning methods for LLMs, multimodal LLMs, knowledge-based LLMs, extension of LLMs, LLMs applications, and more. The repository aims to help individuals quickly understand the advancements and challenges in the field of LLMs through a collection of recent surveys and research papers.

LLM Agents Papers is a repository containing must-read papers on Large Language Model Agents. It covers a wide range of topics related to language model agents, including interactive natural language processing, large language model-based autonomous agents, personality traits in large language models, memory enhancements, planning capabilities, tool use, multi-agent communication, and more. The repository also provides resources such as benchmarks, types of tools, and a tool list for building and evaluating language model agents. Contributors are encouraged to add important works to the repository.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

This repository, Awesome-LLM-Survey, serves as a comprehensive collection of surveys related to Large Language Models (LLM). It covers various aspects of LLM, including instruction tuning, human alignment, LLM agents, hallucination, multi-modal capabilities, and more. Researchers are encouraged to contribute by updating information on their papers to benefit the LLM survey community.

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

Awesome-LLM-in-Social-Science is a repository that compiles papers evaluating Large Language Models (LLMs) from a social science perspective. It includes papers on evaluating, aligning, and simulating LLMs, as well as enhancing tools in social science research. The repository categorizes papers based on their focus on attitudes, opinions, values, personality, morality, and more. It aims to contribute to discussions on the potential and challenges of using LLMs in social science research.

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

A tool for designing nanocarrier systems, tailored to drugs and patient profiles.

Ensures software performance meets organizational standards and expectations.

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

I'm an always-on-call cloud expert, specializing in solving GCP problems.

Detailed technical diagram and graph assistant

Home Hemodialysis Helper for NxStage system. Step-by-step guidance, help for tricky situations, and voice interaction recommended.

Heating industry expert with 20 years of experience, knowledgeable in Beijing's policies.

🖥️ Hackintosh Specialist and Mac adaptations expert, providing advice and technical solutions / Especialista en Hackintosh y adaptaciones para Mac, brindando asesoría y soluciones técnicas. 🛠️

Je suis un assistant IA pour développeurs, spécialisé dans la fourniture de conseils techniques et de solutions de codage basées sur les meilleures pratiques et les tendances actuelles du développement logiciel.

Searches Mastodon for accounts matching a user's interests.

Expert in suggesting literature based on topics, with detailed insights.

Enthusiastic YouTuber GPT finding trending YouTube music links in ballads, R&B, hip hop

I'm an expert in classic literature, here to inspire and guide your reading journey with joy and insight!

Movie recommender for cannabis enthusiasts, laid-back and urban.

Music recommender based on user's song preferences.

Efficient movie recommender with user-driven synopsis options.

A no-nonsense gift recommender with direct Amazon links.

Dispensary-focused cannabis strain recommender with live menu access

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