Project for my thesis; Social Recommender System
-
Updated
Jun 24, 2017 - CSS
Project for my thesis; Social Recommender System
This is a project on Reactive Programming using Scala and Machine Learning using Apache Spark MLib
This JavaScript engine recommends openings based on individual play style. Users can specify whether they are looking for an aggressive/passive opening or an open/closed game. They can also specify whether they like gambits and how deep into theory they'd like to go. Chess opening descriptions are scraped from Wikipedia.
Personalized course recommendation system----R language
A website that finds the ideal movie for you!
Live coding session presented at RecSys 2020
A web application that uses a recommendation system to curate Spotify playlists for you based on your friends’ music tastes.
A movie recommendation program that utilizes user data to match and recommend the user with movies and films that it thinks the user will enjoy.
A content Based Movie Recommender System
This is a movie recommendation system project that I developed to put into practice some Machine Learning techniques, so the goal is for the user to choose a movie that have already been watched and receive the recommendation of new movies related to the chosen one.
Recommendation system for B2B hiring (FYP)
An Indian fashion e-commerce website built using the Django web framework. It provides various functionalities, including a rating and review system with a recommender system build using a matrix factorization-based algorithm(SVD).
Recommender system and search engine for scholarly articles/research papers using Doc2Vec
django app for analysing the feedback whether it is positive or negative. It is trained using Logistic regression ML algorithm
Federated Meta-Learning: a concept that allows everyone to benefit from the data that is generated through machine learning libraries.
Add a description, image, and links to the recommender-system topic page so that developers can more easily learn about it.
To associate your repository with the recommender-system topic, visit your repo's landing page and select "manage topics."