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.

Deployed Product Recommendation Model using collaborative filtering.

Personalised and popularity-based movie recommendations.

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

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.

Recommendation System & it's types

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

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

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.

Recommendation systems for e-commerce sites

Movie Recommendation Anytime Anywhere

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.

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