Building Recommendation Model for the electronics products of Amazon

Accelerating Recommender model training by leveraging popular choices -- VLDB 2022

This is a book recommendation engine built using a hybrid model of Collaborative filtering, Content Based Filtering and Popularity Matrix.

This repository will explain the basic implementation of different types of Recommendation systems using python.

Movie recommendation system based on popularity and also using KNN and Cosine similarity. 🎥🍿

This project essentially recommends books. The 'Top 50' page uses popularity based recommender, while the 'Recommendation' page uses collaborative based recommender to recommend 5 most similar books based on user input.

This project is about Building a reliable Book Recommendation system through datasets provided,

Personalised and popularity-based movie recommendations.

Deployed Product Recommendation Model using collaborative filtering.

Deep Learning is a technology used in machine learning and is applied to a number of signal and image applications. The main purpose of the work presented is to apply the concept of a Deep Learning algorithm namely, Convolutional Neural Networks (CNN) in image classification. A recommendation engine filters the data using different algorithms an…

Recommendation System & it's types

Recommendation systems for e-commerce sites

A recommendation model which finds popular movies according to votes and ratings given to each movie, recommends movies to the user according to the user's previous interactions using K-means Clustering and cosine similarity and also suggests movies to the user based on the likes of similar other users in the dataset using Pearson similarity index.

Book Recommendation System - Popularity Based and Collaborative Filtering Based

Building a recommender engine that reviews customer ratings and purchase history to recommend items and improve sales.

Another interesting use-case of TuriCreate in Machine Learning i.e. Song Recommender System.

Popularity based Recommendation System, Content Based Recommendation System, Cosine Similarity

An end-to-end Course Recommendation System that uses deep learning for personalized suggestions. It combines popularity filtering, frequently bought-together logic, and top educator rankings. The system is built with Python and TensorFlow and deployed using Flask for real-time interaction.

Movie Recommendation Anytime Anywhere

Projects developed under the Data Mining II college chair during the 2019/2020 school year