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filtering** to recommend the movies \nwhich have a higher correlation with the movie which one want's to compare.\n\n# Dependencies explained\nTwo most ubiquitous types of recommender systems are **Content-Based Filtering** and **Collaborative Filtering (CF)**. \n\n\n**Collaborative filtering** produces recommendations based on the knowledge of users’ attitude to items, that is it uses the “wisdom of the crowd” to recommend items. \n* The algorithm has a very interesting property of being able to do feature learning on its own, which means that it can start to learn for itself what features to use.\n* CF can be divided into Memory-Based Collaborative Filtering and Model-Based Collaborative filtering.\n\n**Content-based recommender systems** focus on the attributes of the items and give you recommendations based on the similarity between them.\n\n# Data\nWe will use **MovieLens dataset**, which is one of the most common datasets used when implementing and testing recommender engines. \nIt contains **100k movie ratings** from **943 users** and a selection of **1682 movies.** \n\n\nTo download the dataset : [Click Here](http://files.grouplens.org/datasets/movielens/ml-100k.zip)\n\n# Objective\nPredict how a user will rate a movie, given ratings on other movies and from other users.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchiraag-kakar%2Frecommender","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchiraag-kakar%2Frecommender","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchiraag-kakar%2Frecommender/lists"}