Building Recommendation Model for the electronics products of Amazon

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.

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

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

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.

A book recommender web app which uses popularity based technique and collaborative filtering based technique for making the recommendations.

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

Movie Recommendation Anytime Anywhere

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.

Personalised and popularity-based movie recommendations.

Deployed Product Recommendation Model using collaborative filtering.

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

Book Recommendation System - Popularity Based and Collaborative Filtering Based

This work involved building a pipeline of recommender systems comprising of Popularity based recommender, KNN similarity based Clustering recommender, Item-Item association based recommender, Bi-Partite graph based association recommender, Neural Graph based Collaborative Filtering and Neural Embedding based Collaborative filtering.

Recommendation System & it's types

Popularity based, Content based recommender & Colaborative Filtering systems

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…

This Python project shows how to build a content based recommendation system. Data is related to movies.