Topic: recommendations Goto Github
Some thing interesting about recommendations
Some thing interesting about recommendations
recommendations,A UI library for Algolia Recommend, available for Vanilla JavaScript and React.
Organization: algolia
Home Page: https://www.algolia.com/doc/ui-libraries/recommend/introduction/what-is-recommend/
recommendations,Course on recommender systems conducted at the Faculty of Computer Science, National Research University - Higher School of Economics. Academic year 2022-2023.
User: anamarina
recommendations,Recommendations for secure applications development with Rust
Organization: anssi-fr
Home Page: https://anssi-fr.github.io/rust-guide
recommendations,📚🐦 Patrick Collison on Twitter: Which five books have influenced you the most? (In terms of shaping your worldview.)
User: arikroc
Home Page: https://twitter.com/patrickc/status/929862403763798016
recommendations,Amazon Personalize Langchain extensions to support invoking and retrieving personalized recommendations from your Amazon Personalize resources
Organization: aws-samples
Home Page: https://aws.amazon.com/personalize/
recommendations,Bazaarvoice's Swift SDK
Organization: bazaarvoice
recommendations,An easy and efficient tool to build sequential recommendation system utilizing SASRec
User: beomso0
Home Page: https://ezsasrec.netlify.app
recommendations,🎥 Simple Python implementation of Funk SVD for MovieLens movie collaborative recommendations.
User: bruunand
recommendations,BuiltOn Sneakers Demo Store
Organization: builtondev
recommendations,Constructor.io JavaScript client for web applications
Organization: constructor-io
Home Page: https://constructor.io
recommendations,CORE is a plug-and-play conversational agent for any recommender system.
Organization: core-labet
Home Page: https://jinjiarui.github.io/core/
recommendations,Some recommendations regarding software for desktop and mobile 🔝
User: d3sox
Home Page: https://awesome-software.d3sox.me/
recommendations,:+1::-1: A recommendation engine using Likes and Dislikes for your Ruby app
User: davidcelis
Home Page: http://davidcelis.github.io/recommendable/
recommendations,A library class for WordPress plugins to tweak the results of the WordPress Plugins API with custom plugin recommendations.
User: deckerweb
recommendations,Внедрение и эксплуатация PT Application Inspector. Подробнее: https://habr.com/ru/company/pt/blog/557142/
Organization: devopshq
recommendations,Amazon Recommendation Scraper
Organization: digitalmethodsinitiative
recommendations,Design and Implementation of a File Recommendation System Using Collaborative Filtering and Content-Based Recommendation for the Nextcloud Platform
User: doganoo
Home Page: https://www.dogan-ucar.de/recommendationassistant/
recommendations,Let's discover films.
User: edizaguirre
Home Page: https://www.rosebudrecs.com
recommendations,Repository hosting code used to reproduce results in "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152).
Organization: facebookresearch
recommendations,The code for the paper "MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation" (ACM MM'23).
User: gimpong
recommendations,Java-Based Context-aware Recommendation Library
User: irecsys
Home Page: https://carskit.github.io/
recommendations,Template with best practices for building sustainable GitHub repositories
User: jlcanovas
recommendations,Tune Your Own Spotify Recommendation Algorithm
User: knightspore
Home Page: https://selecta.ciaran.co.za/
recommendations,Dartmouth Course Reviews, Rankings, and Recommendations
Organization: layuplist
Home Page: https://layuplist.com
recommendations,recommender system tutorial with Python
User: lsjsj92
recommendations,Laravel Livewire Best Practices
User: michael-rubel
recommendations,A diverse collection of privacy, security, and FOSS resources in English and Arabic, encompassing tools, extensions, operating systems, encryption, and more. An invaluable repository for bolstering digital security and privacy.
User: mrm8brh
recommendations,🐙 Alternative course catalog for Karlsruhe Institute of Technology
User: muety
Home Page: https://kitsquid.de
recommendations,A self-assessment tool by @NC3-LU to help business owners implement a better cybersecurity strategy.
Organization: nc3-lu
Home Page: https://fit4cybersecurity.nc3.lu
recommendations,Built-in Movie Graph Example
Organization: neo4j-graph-examples
Home Page: https://sandbox.neo4j.com/?usecase=movies
recommendations,Neo4j Graph Example Movies Recommendation
Organization: neo4j-graph-examples
Home Page: https://sandbox.neo4j.com?usecase=recommendations
recommendations,List of awesome apps and website for entertainment.
User: oshekharo
recommendations,🛍 A real-world e-commerce dataset for session-based recommender systems research.
Organization: otto-de
Home Page: https://www.kaggle.com/datasets/otto/recsys-dataset
recommendations,Resources recommended by members of our community. We welcome contributions, so feel free to send us pull requests adding things to the list! The resources in this list automatically get pushed to the http://progdisc.club/resources page.
Organization: progdisc
recommendations,Market basket recommendations using association rules and apriori
User: ramonsaraiva
recommendations,Movie Recommendation System: Project using R and Machine learning
User: rpita623
recommendations,A unified, comprehensive and efficient recommendation library
Organization: rucaibox
Home Page: https://recbole.io/
recommendations,This is a repository of public data sources for Recommender Systems (RS).
Organization: rucaibox
Home Page: https://recbole.io/
recommendations,Recommendation engine in Java. Based on an ALS algorithm (Apache Spark). Train a new model after N seconds.
User: sebsui
recommendations,Simple CLI Tool For Generating Available Telegram Usernames
User: sepandhaghighi
recommendations,Frequent Itemsets via Apriori Algorithm Apriori function to extract frequent itemsets for association rule mining We have a dataset of a mall with 7500 transactions of different customers buying different items from the store. We have to find correlations between the different items in the store. so that we can know if a customer is buying apple, banana and mango. what is the next item, The customer would be interested in buying from the store.
User: shishir349
recommendations,Sci-Fi worth consuming
User: sindresorhus
recommendations,Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Organization: sisinflab
recommendations,Recommendation engines for developers, easy as π. https://tamber.com
Organization: tamber
recommendations,Anime, Manga, & Novel Recommender Website and Application - User and Content-Based Recommendation using Anilist Account (Android and Web). Recommends Anime, Manga (Manhwa, Manhua, One Shots), Light Novel (LN).
User: u-kuro
Home Page: https://kanshi.vercel.app
recommendations,Privacy resources for the layperson. Highlights resources, tools, VPNs, search engines, articles, books, and dark patterns.
User: vlvagerviwager
recommendations,The code for the paper "RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction" (WWW 24 short paper)
User: yushenli807
recommendations,Ten thousand books, six million ratings
User: zygmuntz
Home Page: http://fastml.com/goodbooks-10k
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Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.