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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.

License: Other

C# 0.45% HTML 0.78% CSS 0.01% ASP 0.01% JavaScript 0.86% Python 88.83% Batchfile 0.01% C 2.55% C++ 1.29% Fortran 0.06% Makefile 0.01% MATLAB 0.02% TeX 0.14% PowerShell 0.01% Tcl 5.00% Dockerfile 0.01%
asp-net mvc5 progressive-web-app ajax webapplication pwa-apps aspnet-mvc sql linq css

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