jjoej15/letterboxd-recs

Web app that uses web scraping to give film recommendations using an SVD collaborative filtering model for any Letterboxd user or a recommendation for two using Blend mode. Can include filters such as film popularity, film genre, and films in user's watchlist.

Language: Python - Size: 33.1 MB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

unclebrod/YelpRecommender

The goal of this project was to build an explicit recommender system using collaborative filtering for restaurants in Charlotte using Yelp's Open Dataset. I wanted to explore the mechanics of recommendations systems, and explore a new library in Surprise.

Language: Jupyter Notebook - Size: 15.1 MB - Last synced at: 9 months ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 0

DrPoojaAbhijith/Netflix-Recommendation-Engine

Language: Jupyter Notebook - Size: 79.1 KB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

mayankaga94/Collaborative-Filtering

Recommendation system using collaborative filtering on a movie dataset

Language: Jupyter Notebook - Size: 49.8 KB - Last synced at: about 1 year ago - Pushed at: about 5 years ago - Stars: 1 - Forks: 0

daniel-aime/recommender-system-by-daniel-aime

Projet d'étude système de recommendation en utilisant filtrage collaboratif

Language: Python - Size: 15.6 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 11 - Forks: 1

nkarasovd/HSE_Recommender_Systems

:honeybee: Materials and homework assignments for HSE recommender systems course

Language: Jupyter Notebook - Size: 5.99 MB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

Ayoub-etoullali/SVD-Singular-Value-Decomposition

This project demonstrates the application of Singular Value Decomposition (SVD) for image compression using Python and NumPy.

Language: Jupyter Notebook - Size: 921 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 1

it21208/Bachelors-Thesis

𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐭𝐡𝐞 𝐒𝐕𝐃 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐨𝐧 𝐭𝐡𝐞 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐀𝐩𝐚𝐜𝐡𝐞 𝐒𝐩𝐚𝐫𝐤. Υλοποίηση του αλγορίθµου συστάσεων 𝐒𝐕𝐃 στην πλατφόρµα 𝐒𝐩𝐚𝐫𝐤

Language: TeX - Size: 5.98 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

marreche/deep_film

Movie Recommendation System created using Singular Value Decomposition (SVD).

Language: Jupyter Notebook - Size: 12.5 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 1

jainish-jain/thegeniusyou

The Genius You is a growing platform where the user has options to set daily achieving targets and goals as per the duration locked to accomplish his scope for improvement. This platform provides to improves his\her daily targets such as reading, studying, reducing phone usage, etc. This code is developed to Analysis of the goals that are being generated by the users and suggesting the users for the next goal based on previous data of goals. Using a machine-learning algorithm, singular value decomposition (SVD) for the prediction generates to suggest the user.

Language: Jupyter Notebook - Size: 9.77 KB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

crakesh27/Recommender-System

Implementation of various recommendation algorithms such as Collaborative filtering, SVD and CUR-decomposition to predict user movie ratings

Language: Python - Size: 2.33 MB - Last synced at: almost 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 1

ChayannFamali/Recommendation-system-of-films

In this task, a decision was made to build 3 models of recommendation systems based on the MovieLens Small dataset.

Language: Jupyter Notebook - Size: 1.38 MB - Last synced at: 6 months ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

SoamyaAgrawal17/Recommender_System

Implementation and comparison (time, space) of SVD and CUR matrix decomposition algorithms

Language: Java - Size: 1.83 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 0