Best AI tools for< Recommendation Engineer >

Blackbox is an AI-powered code generation, code chat, and code search tool that helps developers write better code faster. With Blackbox, you can generate code snippets, chat with an AI assistant about code, and search for code examples from a massive database.

Chat Blackbox is an AI tool that specializes in AI code generation, code chat, and code search. It provides a platform where users can interact with AI to generate code, discuss code-related topics, and search for specific code snippets. The tool leverages artificial intelligence algorithms to enhance the coding experience and streamline the development process. With Chat Blackbox, users can access a wide range of features to improve their coding skills and efficiency.

Couture.ai is an AI-as-a-service platform that specializes in hyper-scale AI for tailored retail experiences. The platform assists global online retailers and fashion brands in personalizing customer experiences through prediction technology. Couture.ai offers cutting-edge solutions such as Virtual TryOn for visualizing products before purchase, Demand & Assortment Forecasting for inventory management, Live Search for semantic search solutions, and Obelisk Experience Engine for behavior insights-driven tailored experiences across various platforms. The platform aims to elevate customer experiences and optimize business outcomes through AI-driven solutions.

Crossing Minds is a premium recommendation engine that helps businesses personalize their commerce experiences and increase conversions. It offers a range of features including search and discovery, personalized recommendations, and behavior-based recommendations. Crossing Minds is trusted by brands like Inkbox, HobbyLink Japan, and Eventbrite, and has been shown to increase conversion rates by up to 440%.

Gamelight is a revolutionary AI platform for mobile games marketing that helps acquire high-quality users at scale. By analyzing app usage data and users' behavior, Gamelight creates detailed user profiles and delivers personalized game recommendations to enhance the gaming experience. The platform also offers a loyalty program that rewards users for gameplay duration, fostering retention and engagement. With advanced algorithms like ROAS Algorithm, Gamelight identifies users with the highest likelihood of making a purchase on your game, providing a competitive advantage in user acquisition.

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.

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.

Pinecone is a vector database that helps power AI for the world's best companies. It is a serverless database that lets you deliver remarkable GenAI applications faster, at up to 50x lower cost. Pinecone is easy to use and can be integrated with your favorite cloud provider, data sources, models, frameworks, and more.

Jina AI is a company that provides multimodal AI solutions for businesses and developers. Their products include embeddings, rerankers, and prompt engineering tools. Jina AI's mission is to make AI accessible and easy to use for everyone.

SvectorDB is a vector database built from the ground up for serverless applications. It is designed to be highly scalable, performant, and easy to use. SvectorDB can be used for a variety of applications, including recommendation engines, document search, and image search.

Pinecone is a vector database designed to help power AI applications for various companies. It offers a serverless platform that enables users to build knowledgeable AI applications quickly and cost-effectively. With Pinecone, users can perform low-latency vector searches for tasks such as search, recommendation, detection, and more. The platform is scalable, secure, and cloud-native, making it suitable for a wide range of AI projects.

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.

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.

FINIITE AI is a retail and brand marketing solution that uses AI to help businesses grow their sales and improve customer experience. The company's flagship product is a product recommendation engine that uses AI to personalize product recommendations for each customer. FINIITE AI also offers a suite of other AI-powered solutions, including a skin check tool, a customer data insights platform, and a CRM integration. FINIITE AI's solutions are used by businesses of all sizes, from small businesses to large enterprises.

Aidaptive provides an end-to-end Artificial Intelligence (AI) and Machine Learning Platform powering High-Efficiency Commerce. Its autonomous intelligence platform for digital commerce uses world-leading machine learning technology to upgrade businesses from data-driven to intelligence-driven.

Dev Radar is an open-source, AI-powered news aggregator that helps users stay up to date with the latest trends in software development. It provides curated articles on various programming languages and frameworks, offering valuable insights for developers. The platform leverages AI algorithms to discover and recommend relevant content, making it a valuable resource for staying informed in the rapidly evolving tech industry.

Generative.ai is an AI tool designed for Salesforce consultants to enhance productivity and efficiency in creating solutions, estimates, and proposals. The tool leverages AI technology to generate detailed proposals in minutes, provide commercial insights, and recommend product features based on extensive data processing. It aims to streamline the proposal creation process and improve accuracy through AI-assisted enhancement.

AppSec Assistant is an AI-powered application designed to provide automated security recommendations in Jira Cloud. It focuses on ensuring data security by enabling secure-by-design software development. The tool simplifies setup by allowing users to add their OpenAI API key and organization, encrypts and stores data using Atlassian's Storage API, and provides tailored security recommendations for each ticket to reduce manual AppSec reviews. AppSec Assistant empowers developers by keeping up with their pace and helps in easing the security review bottleneck.

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

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

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.

VectorETL is a lightweight ETL framework designed to assist Data & AI engineers in processing data for AI applications quickly. It streamlines the conversion of diverse data sources into vector embeddings and storage in various vector databases. The framework supports multiple data sources, embedding models, and vector database targets, simplifying the creation and management of vector search systems for semantic search, recommendation systems, and other vector-based operations.

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.

The Python Redis Vector Library (RedisVL) is a tailor-made client for AI applications leveraging Redis. It enhances applications with Redis' speed, flexibility, and reliability, incorporating capabilities like vector-based semantic search, full-text search, and geo-spatial search. The library bridges the gap between the emerging AI-native developer ecosystem and the capabilities of Redis by providing a lightweight, elegant, and intuitive interface. It abstracts the features of Redis into a grammar that is more aligned to the needs of today's AI/ML Engineers or Data Scientists.

Superlinked is a compute framework for information retrieval and feature engineering systems, focusing on converting complex data into vector embeddings for RAG, Search, RecSys, and Analytics stack integration. It enables custom model performance in machine learning with pre-trained model convenience. The tool allows users to build multimodal vectors, define weights at query time, and avoid postprocessing & rerank requirements. Users can explore the computational model through simple scripts and python notebooks, with a future release planned for production usage with built-in data infra and vector database integrations.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

A self-hostable bookmark-everything app with a touch of AI for data hoarders. Features include bookmarking links, taking notes, storing images, automatic fetching for link details, full-text search, AI-based automatic tagging, Chrome and Firefox plugins, iOS and Android apps, dark mode support, and self-hosting. Built to address the need for archiving and previewing links with automatic tagging. Developed by a systems engineer to stay connected with web development and cater to personal use cases.

allms is a versatile and powerful library designed to streamline the process of querying Large Language Models (LLMs). Developed by Allegro engineers, it simplifies working with LLM applications by providing a user-friendly interface, asynchronous querying, automatic retrying mechanism, error handling, and output parsing. It supports various LLM families hosted on different platforms like OpenAI, Google, Azure, and GCP. The library offers features for configuring endpoint credentials, batch querying with symbolic variables, and forcing structured output format. It also provides documentation, quickstart guides, and instructions for local development, testing, updating documentation, and making new releases.

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.

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.

MicroLens is a content-driven micro-video recommendation dataset at scale. It provides a large dataset with multimodal data, including raw text, images, audio, video, and video comments, for tasks such as multi-modal recommendation, foundation model building, and fairness recommendation. The dataset is available in two versions: MicroLens-50K and MicroLens-100K, with extracted features for multimodal recommendation tasks. Researchers can access the dataset through provided links and reach out to the corresponding author for the complete dataset. The repository also includes codes for various algorithms like VideoRec, IDRec, and VIDRec, each implementing different video models and baselines.

AI Product Home is a repository dedicated to collecting various AI commercial or open-source products. It provides assistance in submitting issues, self-recommendation, correcting resources, and more. The repository also features AI tools like Build Naidia, Autopod, Rytr, Mubert, and a virtual town driven by AI. It includes sections for AI models, chat dialogues, AI assistants, code assistance, artistic creation, content creation, and more. The repository covers a wide range of AI-related tools and resources for users interested in AI products and services.

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.

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.

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.

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.

Cool-GenAI-Fashion-Papers is a curated list of resources related to GenAI-Fashion, including papers, workshops, companies, and products. It covers a wide range of topics such as fashion design synthesis, outfit recommendation, fashion knowledge extraction, trend analysis, and more. The repository provides valuable insights and resources for researchers, industry professionals, and enthusiasts interested in the intersection of AI and fashion.

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AquaAirAI is a specialized assistant that compares air and water quality across cities and regions, providing insightful reports and recommendations based on comprehensive environmental data analysis from Excel files.

AgriGPT is an intelligent agricultural advisor designed to enhance productivity and efficiency in farming and agriculture. Its purpose is to provide valuable insights, recommendations, and guidance to farmers and agricultural professionals.

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