Presentation on how to build a recommender system @ PyData, Lisbon, Wed 13 Dec 2017

Recommendation on data from the IBM Watson Studio platform

Concepts for the recommendation of articles . Udacity Data Science Nanodegree Program 3rd Project - Recommendation Engines .

This repository represents several projects completed in IE HST's MS in Business Analytics and Big Data program, Recommendation Engines course.

(Udacity Data Scientist Nanodegree Project) Recommendation functions for collaborative filtering and content-based recommendations

Implementation for two different types of recommendation systems (Content-based and collaborative filtering)

It is a Flask application that will recommend you top 10 movies related to your searched movie.

analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations on new articles they will like.

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

This repository contains a recommender system based on K-Mean Clustering combine with Content Based Filtering on Junior High School in Bandung, Indonesia dataset.

Built article recommendation engines for IBM Watson's platform users. Used collaborative filtering, content-based, ranking and knowledge-based techniques.

The sample code repository leverages Azure Text Analytics to extract key phrases from the product description as additional product features. And perform text relationship analysis with TF-IDF vectorization and Cosine Similarity for product recommendation.

Recommender Systems 2021/2022: Content Based Recommenders Project

Advanced Data Mining Final Project - March 17, 2022

UPNEXT: Content-based and Context-Aware Movie Recommender System

This Repository contains codes for Movie Recommendation System. Algorithms which are used for designing are Content Filtering, Collaborative Filtering and also the combined output of both. Coding is done in Python.

Book Recommendation System- A Web app made using flask framework to recommend your favorite book using content based filtering and cosine similarity metrices.

Performed EDA, created user-article matrix, calculated similarity using dot product, implemented Rank-Based, User-User CF, Content-Based, and Matrix Factorization, evaluated model with precision, recall, and F1-score.

Recommends Anime using Content based filtering (using TFIDF vectorization and sigmoid kernel) and collaborative filtering (using KNN)