Best AI tools for< Recommendation >

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.

Software Recs is a free AI tool designed to provide software recommendations tailored to your specific use case. By entering your email and describing your company's activities and challenges, the tool generates personalized recommendations to assist you in your product search. The platform aims to streamline the process of finding suitable software solutions by leveraging artificial intelligence technology.

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.

Hackers.dev was an AI-powered platform that provided skill-based job recommendations for developers. The platform utilized an embeddings model fine-tuned on 100,000 developer jobs to offer personalized job suggestions. Unfortunately, the website has been discontinued, but it previously offered over 8,000 job listings from 500+ companies. Users could access the service through the website or connect via GitHub or Twitter.

Find My Size is a web application that provides personalized size recommendations for exclusive deals at hundreds of top retailers. Users can input their measurements and preferences to receive tailored suggestions for clothing items that will fit them perfectly. The platform aims to enhance the online shopping experience by helping customers find the right size and style without the need for multiple returns. Find My Size collaborates with various retailers to offer a wide range of products across different categories, including active & sportswear, young contemporary, business & workwear, lingerie & sleepwear, outerwear, maternity wear, plus size apparel, and swimwear.

Find your next book is an AI-powered librarian that provides personalized book recommendations based on your preferences. It uses advanced algorithms to analyze your reading history, interests, and other factors to suggest books that you're likely to enjoy. The platform offers a wide range of genres and authors to choose from, making it easy to find your next favorite read.

PeerVibe is the first platform for your personalized profile with recommendations from colleagues! Sign up to get early access!

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 AI-powered tool provides personalized recommendations for celebrating Mother's Day. It offers thoughtful suggestions based on your input, helping you create a special and meaningful day for your mom. The tool is free to use and respects your privacy, ensuring that your personal information remains confidential.

ResumeDive is an AI-driven tool designed to enhance resumes and optimize job application processes. It provides personalized feedback, job-specific action items, pros and cons analysis, tailored cover letters, and salary estimation. Users can improve their resume by tailoring skills to job descriptions, meeting ATS standards, and impressing recruiters. The tool offers free audits and affordable credit-based pricing options for users to access its features.

Saasquarepro is an AI tool that helps businesses find the best SaaS solutions tailored to their needs. By analyzing business requirements, size, and industry, the AI algorithm provides personalized matches, saving time and effort. The tool offers a diverse range of SaaS products, from CRM to project management, ensuring up-to-date recommendations based on the latest trends. With efficiency at its core, users can say goodbye to endless searching and comparing, receiving swift and accurate recommendations for free. Saasquarepro aims to boost SaaS product visibility and help businesses reach more customers effortlessly.

BooksAI is an AI-powered tool that provides book summaries, recommendations, and more. With over 40 million book summaries available, BooksAI makes it easy to discover new books and learn about your favorites. BooksAI's summaries are concise and easy to understand, making them perfect for busy professionals, students, and anyone who wants to learn more about the world's greatest books.

My Perfect Hairstyle is an AI-powered tool that helps users find their perfect hairstyle. By utilizing advanced artificial intelligence algorithms, the application analyzes facial features and suggests hairstyles that best suit the user's unique characteristics. Users can experiment with different styles virtually before making a decision, saving time and effort. Whether you're looking for a new haircut, color, or style, My Perfect Hairstyle provides personalized recommendations tailored to your preferences.

BookSurfAi is an AI-powered book recommendation tool that helps users discover their next favorite book. By leveraging artificial intelligence technology, the application provides personalized reading suggestions based on individual preferences and reading habits. BookSurfAi aims to enhance the reading experience by offering tailored recommendations that cater to each user's unique tastes and interests. With a user-friendly interface, BookSurfAi makes it easy for book lovers to explore new literary works and expand their reading horizons.

Maimovie is an AI-powered movie and TV show search engine that helps users find content based on their specific moods or contexts. It offers an infinite number of personal recommendations based on user preferences, as well as live-updated AI curation of movie and TV show rankings trending on popular streaming services. Maimovie provides detailed information about each movie and TV show, including plot, available streaming services, ratings, cast, crew, and similar movies.

Voxal AI is an AI-powered chatbot solution designed to enhance sales, customer support, and user engagement on websites. It offers a range of features including multiple AI models, A/B testing, cross-platform compatibility, multilingual support, advanced analytics, customization options, and multi-platform integration. Voxal AI is suitable for various industries such as e-commerce, e-learning, and SaaS, and can be used for tasks such as product recommendations, lead qualification, and automated customer support.

MovieWiser is an AI-powered movie and series recommendation engine that provides personalized suggestions based on your current mood. It analyzes your past viewing history, preferences, and current emotional state to curate a list of movies and series that are tailored to your specific needs. With MovieWiser, you can easily find the perfect movie or series to watch, whether you're looking for something to relax and unwind with, something to make you laugh, or something to get your adrenaline pumping.

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.

Gift Wizard is an AI-powered gift suggestion tool that helps users find the perfect gift for any occasion or recipient. By answering a few simple questions about the recipient, the tool's intelligent algorithm provides personalized gift ideas tailored to their preferences. It is easy to use, offers thoughtful and relevant gift ideas, and is powered by AI technology. Gift Wizard is free for everyone, does not require sign-up, and provides real-time product data to enhance the gift-giving experience.

Threadsense is an AI Fashion Assistant that provides personalized fashion recommendations and outfit suggestions powered by cutting-edge AI technology. Users can generate perfect outfits for any occasion, filter through endless outfit possibilities, and upload their wardrobe to let AI generate personalized outfits using their own clothes. The application aims to help users look stunning every day by offering instant outfit ideas and style suggestions tailored to their preferences.

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.

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

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.

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.

Recommendarr is a tool that generates personalized TV show and movie recommendations based on your Sonarr, Radarr, Plex, and Jellyfin libraries using AI. It offers AI-powered recommendations, media server integration, flexible AI support, watch history analysis, customization options, and dark/light mode toggle. Users can connect their media libraries and watch history services, configure AI service settings, and get personalized recommendations based on genre, language, and mood/vibe preferences. The tool works with any OpenAI-compatible API and offers various recommended models for different cost options and performance levels. It provides personalized suggestions, detailed information, filter options, watch history analysis, and one-click adding of recommended content to Sonarr/Radarr.

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.

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.

This repository contains the source code for the SIGIR 2023 paper 'Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited'. It provides resources for evaluating foundation, transferable, multi-modal, and LLM recommendation models, along with datasets, pre-trained models, and training strategies for IDRec and MoRec using in-batch debiased cross-entropy loss. The repository also offers large-scale datasets, code for SASRec with in-batch debias cross-entropy loss, and information on joining the lab for research opportunities.

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.

This repository provides recommendations for VPN services, specifically focusing on bypassing internet restrictions and accessing blocked content. It includes information on various VPN providers, their pricing, available locations, protocols, and additional features. Users can find details on high-speed, stable, and cost-effective VPN options to suit their needs, along with tips for selecting multiple providers for backup purposes. The repository aims to guide users, especially beginners, in choosing VPN services that offer good value, stability, and customer support.

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.

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.

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.

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.

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.

Kaytu is an AI platform that enhances cloud efficiency by analyzing historical usage data and providing intelligent recommendations for optimizing instance sizes. Users can pay for only what they need without compromising the performance of their applications. The platform is easy to use with a one-line command, allows customization for specific requirements, and ensures security by extracting metrics from the client side. Kaytu is open-source and supports AWS services, with plans to expand to GCP, Azure, GPU optimization, and observability data from Prometheus in the future.

Auto_Jobs_Applier_AIHawk is an AI-powered job search assistant that revolutionizes the job search and application process. It automates application submissions, provides personalized recommendations, and enhances the chances of landing a dream job. The tool offers features like intelligent job search automation, rapid application submission, AI-powered personalization, volume management with quality, intelligent filtering, dynamic resume generation, and secure data handling. It aims to address the challenges of modern job hunting by saving time, increasing efficiency, and improving application quality.

This repository contains a chatbot tool powered by AI technology. The chatbot is designed to interact with users in a conversational manner, providing information and assistance on various topics. Users can engage with the chatbot to ask questions, seek recommendations, or simply have a casual conversation. The AI technology behind the chatbot enables it to understand natural language inputs and provide relevant responses, making the interaction more intuitive and engaging. The tool is versatile and can be customized for different use cases, such as customer support, information retrieval, or entertainment purposes. Overall, the chatbot offers a user-friendly and interactive experience, leveraging AI to enhance communication and engagement.

ClicShoppingAI is a powerful open-source Ecommerce solution that supports B2B, B2C, and B2B-B2C. Integrated with cutting-edge generative artificial intelligence systems like Gpt and Ollama, it helps merchants increase turnover and competitiveness for free. With AI capabilities, it optimizes inventory, offers personalized recommendations, and provides top-notch customer service. The solution is modular, lightweight, and user-friendly, with a seamless, responsive design for all devices. Installation is easy, empowering ongoing development through community support. Features include GPT API integration, generative AI functionalities, real-time safety stock predictive, WYSIWYG product description creation, image editor management, full SEO optimization, payment and shipping modules, extension system, GDPR compliance, multi-language support, and more.

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

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

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

Expert in Bollywood movies, provides tailored recommendations.

Finds the best Shopify app for your requirements and budget

Your Go-To Guide for Personalized Movie, TV, Anime, and Song Recommendations.

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

The best non-fiction book recommendation assistant.

Get recommendations based on your phone's Homescreen screenshot! Just add the screenshot in here for analysis 📱🧐

Get recommendations of best AI & no-code tools you can use for any task

Your go-to GPT for tailored book recommendations.

Your go-to buddy for personalized bike recommendations.

Expert on movies & TV shows, providing tailored recommendations, reviews, and trivia.

Suggests recommendations on 'Experience' aspect of E-E-A-T

Your go-to guide for all things books, from recommendations to analysis.

A friendly, engaging guide for personalized book recommendations.

With deep knowledge of content that is available on Hulu, I specialize in enhancing your Hulu viewing experience with personalized recommendations, insightful trivia, and contextual information.