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https://github.com/chiraag-kakar/recommender
Implementing Model-Based CF using SVD & Memory-Based CF by computing cosine similarity on MovieLens dataset
https://github.com/chiraag-kakar/recommender
collaborative-filtering movie movielens-dataset project ratings recommender-system
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Implementing Model-Based CF using SVD & Memory-Based CF by computing cosine similarity on MovieLens dataset
- Host: GitHub
- URL: https://github.com/chiraag-kakar/recommender
- Owner: chiraag-kakar
- License: mit
- Created: 2020-08-16T11:12:52.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-08-16T14:26:20.000Z (over 4 years ago)
- Last Synced: 2023-08-07T19:28:02.633Z (over 1 year ago)
- Topics: collaborative-filtering, movie, movielens-dataset, project, ratings, recommender-system
- Homepage:
- Size: 4.88 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Recommender
Recommender is a movie recommendation system which makes use of **memory based & model based collaborative filtering** to recommend the movies
which have a higher correlation with the movie which one want's to compare.# Dependencies explained
Two most ubiquitous types of recommender systems are **Content-Based Filtering** and **Collaborative Filtering (CF)**.**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.
* 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.
* CF can be divided into Memory-Based Collaborative Filtering and Model-Based Collaborative filtering.**Content-based recommender systems** focus on the attributes of the items and give you recommendations based on the similarity between them.
# Data
We will use **MovieLens dataset**, which is one of the most common datasets used when implementing and testing recommender engines.
It contains **100k movie ratings** from **943 users** and a selection of **1682 movies.**To download the dataset : [Click Here](http://files.grouplens.org/datasets/movielens/ml-100k.zip)
# Objective
Predict how a user will rate a movie, given ratings on other movies and from other users.