Topic: recommendation-algorithm Goto Github
Some thing interesting about recommendation-algorithm
Some thing interesting about recommendation-algorithm
recommendation-algorithm,Contextual Movie Recommender System built on mobile with the aim of showing interest based movies from the huge amount of data based on rating of user and critics which would be crawled from the specified website. Recommender System are primarily directed towards individuals who lack sufficient personal experience or competence to evaluate the potentially overwhelming number of alternative items that a mobile app may offer. We are creating a mobile app based application for contextual recommendation of movies based on Expert Collaborative Filtering [CF]. The application not only recommends movies, but the user can also watch movie show times from cinema relative to recommended movies. The application, however, is much more than yet another implementation of Expert CF. you can offer personalized recommendations on a device with 100% privacy guarantees, but you can also possibly run a recommendation algorithm on the device, with minimum intervention from the server-side. The server is in charge of compiling all the public information available on the mobile app by crawling critic websites like [ Rottentomatoes, IMDB…]. It also gathers information about local cinemas and their schedules. All this information, which again is public, is stored in a SQL database and shared through a RESTful API with devices. The device, connects to the server and syncs a local database through the RESTful API. Once this is done, all needed information is local on the device. Plus... all the personal information about the user (i.e. ratings on movies in this case). The recommendation algorithm can also possibly run locally and return results in a reasonable time because the set of experts is limited. Another important addition to the application is that we have added contextual features. The recommendations you will get on the app can possibly depend on your location. Therefore, it will recommend things that match your taste according to the expert-based prediction but can also be available in a cinema.
User: abdullol
recommendation-algorithm,Admission Prediction based on GPA and GRE scores
User: anjanatiha
recommendation-algorithm,Movie Recommendation Engine based on a K-Means clustering algorithm of IMDB public data. Node server for user interface.
User: arthurkuhn
recommendation-algorithm,An easy and efficient tool to build sequential recommendation system utilizing SASRec
User: beomso0
Home Page: https://ezsasrec.netlify.app
recommendation-algorithm,Projects for Social Gaming course in Technical University of Munich
User: berkerol
recommendation-algorithm,Part of a presentation I gave at a career day at a local junior high. This is an example of an algorithm that will recommend movies.
User: brockthebear
recommendation-algorithm,Research on User Profile and Resource Recommendation of Online Learning Platform Based on Collaborative Filtering Algorithm
User: capcomin
Home Page: http://uwa.zxy.link
recommendation-algorithm,A collection of resources for Recommender Systems (RecSys)
User: chihming
recommendation-algorithm,Smart tutor for arithmatic
User: concavegit
recommendation-algorithm,Code the Curve 2020
User: cviaai
Home Page: https://coronomy.online
recommendation-algorithm,This is a new deep learning model for recommender system, which we called PHD
User: daicoolb
recommendation-algorithm,Recommendation System for question / requirement and recommended answer from knowledge base
User: daniel-julio-iglesias
Home Page: https://github.com/daniel-julio-iglesias/qnarecom
recommendation-algorithm,Recommends movies based on user input and a a pre-trained NMF-model with a browser-based user interface (Flask). Optionally outputs movie trailers for recommendations from YouTube.
User: dannyibo
Home Page: http://ec2-18-194-75-197.eu-central-1.compute.amazonaws.com/
recommendation-algorithm,pyRecLab is a library for quickly testing and prototyping of traditional recommender system methods, such as User KNN, Item KNN and FunkSVD Collaborative Filtering. It is developed and maintained by Gabriel Sepúlveda and Vicente Domínguez, advised by Prof. Denis Parra, all of them in Computer Science Department at PUC Chile, IA Lab and SocVis Lab.
User: gasevi
recommendation-algorithm,:zap: A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize
User: gbolmier
recommendation-algorithm,Finding recommendations between them all. Work in progress.
User: goldbattle
recommendation-algorithm,Recommender as a service.
User: grpro
Home Page: https://grpro.github.io/recommender_lab/
recommendation-algorithm,A TensorFlow recommendation algorithm and framework in Python.
User: jfkirk
recommendation-algorithm,商品关联关系挖掘,使用Spring Boot开发框架和Spark MLlib机器学习框架,通过FP-Growth算法,分析用户的购物车商品数据,挖掘商品之间的关联关系。项目对外提供RESTFul接口。
User: jingpeicomp
recommendation-algorithm,this is a program that recommends offers for customers based on a given database and decides which offer is suitable for them
User: mahmoudjobeel1
recommendation-algorithm,[PY]thon [R]ec[O]mmender [S]ystems library
User: makgyver
recommendation-algorithm,🎥 Movie Recommender AI System
User: martinkondor
Home Page: https://martinkondor.github.io/MovieRecommender/
recommendation-algorithm,Personal Event Recommendation System & Ticket Search Engine
User: moonsulong
recommendation-algorithm,A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.
User: morningsky
recommendation-algorithm,Data Science undergraduate thesis presentation.
User: naranjja
Home Page: https://naranjja.github.io/thesis/
recommendation-algorithm,A book recommendations application that works on the Dash framework and implements content based filtering using TF-IDF and cosine similarity.
User: niharika412
recommendation-algorithm,Implementations of the EigenRec algorithm
User: nikolakopoulos
recommendation-algorithm,MIT xPRO Data Science Course Movie Recommendations Case Study
User: ozguramac
recommendation-algorithm,matrix-factorization is a light-weight program written in python language for performing basic operations for matrix factorization-based collaborative filtering. I have plans to create a python module from this repository in the future. If you want to contribute to this project, you are most welcome.
User: p0l4r
recommendation-algorithm,[ Algorithms ] - Search-, Sort-, and Recommendation Algorithms implemented in C#, Python, Java and Swift
User: philippmos
recommendation-algorithm,Investigated Lyft riders’ data set, by performing data wrangling, conducting exploratory data analysis, and building statistical machine-learned model, using python packages, to determine KPIs, that guide riders’ cancellation decision
User: pranavd0828
recommendation-algorithm,CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)
User: qiaoguan
Home Page: https://github.com/qiaoguan/deep-ctr-prediction
recommendation-algorithm,Board game recommendation engine
Organization: recommend-games
Home Page: https://recommend.games/
recommendation-algorithm,Best Practices on Recommendation Systems
Organization: recommenders-team
Home Page: https://recommenders-team.github.io/recommenders/intro.html
recommendation-algorithm,A lightweight recommendation algorithm framework based on LycorisNet.
User: rootharold
recommendation-algorithm,
Organization: shaped-ai
recommendation-algorithm,In this Movie Dataset I have used my Basic Knowledge of Recommendation Systems. I have used the Concept of Content -Based Recommendation System to Recommend that If a User Watched a Particular Movie than Which Other Movies He/She is going to like or watch.
User: sharmaroshan
recommendation-algorithm,
User: simon-muenker
Home Page: http://ranker.twon.uni-trier.de
recommendation-algorithm,Experiments on Recommendation systems
User: theolvs
recommendation-algorithm,Machine Learning (EE 5184) in NTU
User: thtang
Home Page: http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17_2.html
recommendation-algorithm,Collaborative filtering recommendation system. Recommendation algorithm using collaborative filtering. Topics: Ranking algorithm, euclidean distance algorithm, slope one algorithm, filtragem colaborativa.
User: tigocaval
Home Page: https://youtu.be/ArGWGn3w788
recommendation-algorithm,Recommender system
User: vwang0
recommendation-algorithm,Code for RecSys'19 paper: Leveraging Post-click Feedback for Content Recommendations
User: whongyi
recommendation-algorithm,:trident: Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.
User: yeaseen
recommendation-algorithm,OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms
User: ylongqi
Home Page: http://www.openrec.ai
recommendation-algorithm,Behavior based video recommendation, global keyboard control and ads block for Youtube
User: ys2843
recommendation-algorithm,PHP and wrapping Redis's sorted set APIs for specializing recommending operations.
User: yuzurus
Home Page: https://packagist.org/packages/yuzuru-s/redis-recommend
recommendation-algorithm,A real-time news scraping and recommendation system
User: ztqsteve
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
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.