GitHub topics: content-based-filtering

Repositories

Janviswa/Movie-Recommender-System

A sleek Streamlit-based movie recommendation system using content-based filtering and TMDB API integration for trailers, posters, and ratings.

Language: Jupyter Notebook - Size: 10.5 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 0 - Forks: 0

fiyandamamuri/Content-Based-Filtering-for-Products-Recommendation

Sistem Rekomendasi Produk menggunakan Content-Based Filtering dari dataset BigBasket. Menggunakan TF-IDF & Cosine Similarity untuk menyarankan produk serupa.

Language: Jupyter Notebook - Size: 0 Bytes - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 0 - Forks: 0

Vankudoth-Saipriya/Restaurant-Recommendation_system

A content-based Restaurant Recommendation System using TF-IDF and cosine similarity. Built with Python, enhanced with search & rating features, and deployed via Streamlit.

Language: Python - Size: 81.1 KB - Last synced at: 8 days ago - Pushed at: 8 days ago - Stars: 0 - Forks: 0

rctruta/music_recommendation_system

Capstone Project for the Applied Data Science Program, MIT Professional Education

Language: Jupyter Notebook - Size: 887 KB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 2 - Forks: 0

albertruaz/Recommendation-System

Language: Python - Size: 11.5 MB - Last synced at: 10 days ago - Pushed at: 10 days ago - Stars: 0 - Forks: 0

Prajjwal6969/Recommender-System-using-Python

A collection of content-based recommendation systems for songs and movies using Python and machine learning.

Language: Jupyter Notebook - Size: 9.2 MB - Last synced at: 10 days ago - Pushed at: 10 days ago - Stars: 0 - Forks: 0

PsiPJ/movie-recommender

A hybrid content-based and collaborative filtering movie recommender system built using Python and Jupyter. Notebook. Designed for showcasing recommender system skills and end-to-end ML workflow.

Language: Jupyter Notebook - Size: 7.34 MB - Last synced at: 12 days ago - Pushed at: 12 days ago - Stars: 0 - Forks: 0

saadamir1/mericloset

MeriCloset: Smart fashion discovery across multiple brands

Language: TypeScript - Size: 5.11 MB - Last synced at: 15 days ago - Pushed at: 15 days ago - Stars: 0 - Forks: 0

bangaji313/Recommender-System-MovieLens

Proyek Sistem Rekomendasi Film dengan Content-Based & Collaborative Filtering. Submission untuk modul Machine Learning Terapan di Coding Camp 2025.

Language: Jupyter Notebook - Size: 1.52 MB - Last synced at: 7 days ago - Pushed at: 15 days ago - Stars: 0 - Forks: 0

sabin74/movie_recommendation_system

A Python-based movie recommendation engine built using the MovieLens Dataset that supports:

Language: Jupyter Notebook - Size: 1.26 MB - Last synced at: 7 days ago - Pushed at: 18 days ago - Stars: 0 - Forks: 0

imtej/Recipe-Reccomendation-System

A personalized recipe recommendation system leveraging TF-IDF encoding and Content-Based filtering technique for dynamic recipe suggestions.

Language: Jupyter Notebook - Size: 878 KB - Last synced at: 7 days ago - Pushed at: 23 days ago - Stars: 0 - Forks: 0

MaklonFR/Movie-Recomendation-System

Submision Dicoding Indonesia - Machine Learning Terapan (Movie Recomendation)

Language: Jupyter Notebook - Size: 681 KB - Last synced at: 24 days ago - Pushed at: 5 months ago - Stars: 3 - Forks: 0

AadrianLeo/Book-Recommendation-System

Book Recommender System using the Book-Crossing dataset. Compares content-based (TF-IDF + cosine similarity) and collaborative filtering (SVD) methods for book recommendations. Includes data cleaning, EDA, and model evaluation (Precision@5, RMSE) in Python.

Language: Jupyter Notebook - Size: 59.7 MB - Last synced at: 21 days ago - Pushed at: 26 days ago - Stars: 0 - Forks: 0

vb8146649/Movie-Recommender-System

The Movie Recommend System is a content-based movie recommendation engine that suggests similar films based on user input. Simply enter the name of a movie you like, and the system will return a curated list of titles that match its themes, genres, or style.

Language: Python - Size: 3.87 MB - Last synced at: 28 days ago - Pushed at: 28 days ago - Stars: 0 - Forks: 0

Sravyatogarla/Movie-recommendation-system

A complete Movie Recommendation System project implementing Popularity-Based, Content-Based, and Collaborative Filtering models using the MovieLens dataset. Built with Python, Pandas, and Plotly, featuring interactive inputs and visualizations.

Language: Jupyter Notebook - Size: 1.1 MB - Last synced at: 21 days ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

vb8146649/Book-Recommend-System

The Book Recommendation System is a content-based filtering project built using Python. It suggests books similar to a user’s input by analyzing textual features like the title, author, genre, and description. This system aims to help readers discover books that match their interests and reading preferences.

Language: Python - Size: 2.93 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

mozayed007/Media-Recommender

A recommendation system for Media Cross Types and Genres

Language: Jupyter Notebook - Size: 148 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

al4744/rec-system

🎵 A Python-based content recommendation system using ML algorithms and matrix factorization techniques to analyze 600k-song dataset. Combines SVD, NMF, Factorization Machines, and Direct Similarity for personalized music suggestions. Handles cold start, optimizes with weighted similarity, and includes tools for visualization & evaluation.

Language: Jupyter Notebook - Size: 1.93 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

CSFelix/recommendation-system

✨ Recommendation Systems Using Diverse Techniques ✨

Language: Jupyter Notebook - Size: 27.4 MB - Last synced at: 7 days ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 0

CSFelix/recommendation-system-mba-usp-esalq

✨ Recommendation Systems Using Diverse Techniques ✨

Language: Jupyter Notebook - Size: 8.36 MB - Last synced at: 3 days ago - Pushed at: 6 months ago - Stars: 2 - Forks: 0

vishal220703/Book-Recommender-System

A Flask-based book recommendation system that suggests similar books using collaborative filtering and precomputed similarity scores.

Language: Jupyter Notebook - Size: 65.2 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

Shaik-Suhail/Book-Recommendation-System

A simple Book Recommendation System that suggests books to users based on similarity scores using content-based filtering. Built with Python, it helps users discover books they might enjoy by analyzing features like title, author, and genre.

Size: 12.8 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

cerqueiraa23/Spotify-Powered-Music-Recommendation-System

A hybrid music recommendation system using Spotify API and Python. Combines audio feature analysis with popularity weighting to suggest personalized tracks.

Size: 3.91 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

HanugaFathurC/book-recommendation-system-ml

An intelligent book recommendation system using machine learning. The system is built with Content-Based Filtering (TF-IDF + Cosine Similarity) and Collaborative Filtering (RecommenderNet with deep learning) to suggest personalized book recommendations.

Language: Jupyter Notebook - Size: 0 Bytes - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

zavarujs/EAFC-2025-Hybrid-Recommendation-System-

Language: Jupyter Notebook - Size: 2.02 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

abburi33/Spotify-Powered-Music-Recommendation-System

A hybrid music recommendation system using Spotify API and Python. Combines audio feature analysis with popularity weighting to suggest personalized tracks.

Size: 1.95 KB - Last synced at: about 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

HrishikeshSuchindra/Tamil-Movie-Recommendation-System-using-NLP

A content-based movie recommendation system using Natural Language Processing (NLP) techniques on Tamil movie reviews. The system utilizes TF-IDF vectorization and cosine similarity to suggest movies based on user preferences or input reviews.

Language: Jupyter Notebook - Size: 7.65 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

RedDotz20/news-content-recommender-system

📰 ArticleHorizon is a smart news article recommender webapp that helps users discover relevant and engaging content tailored to their interests.

Language: TypeScript - Size: 1.56 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

Mohabdo21/HybridRec-ContextEnrichment

An advanced hybrid recommendation system that combines collaborative filtering and content-based filtering approaches, enhanced with temporal awareness and contextual personalization

Language: Python - Size: 2.49 MB - Last synced at: about 1 month ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

BigBeanTheory/Movie-Recommendation-System

A Streamlit-based web app that suggests movies using a content-based filtering model, built with Python, Pandas, and Scikit-learn. Leverages TMDB data to recommend films based on genres, keywords, cast, and crew, with posters fetched via API.

Language: Jupyter Notebook - Size: 9.59 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

mlkav/movie-recommendation-system

Language: HTML - Size: 392 KB - Last synced at: 8 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

docsallover/music-recommendation

Machine Learning Music Recommendation System: Hybrid Approach (Content & SVD) with Flask

Language: Python - Size: 4.08 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

evelyncy96/Movie-Recommendation-System

We create movie recommendation system through demographic filtering, content-based filtering, collaborative filtering, and hybrid engine.

Language: Python - Size: 104 KB - Last synced at: about 1 month ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

philiptitus/Collaborative-Book-Recommender

Made use of the content-based filtering algorithm to make a book recommender model

Language: Jupyter Notebook - Size: 10.7 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

mrnust/Recommendations-System

Content Based Filtering

Language: Jupyter Notebook - Size: 21.5 KB - Last synced at: 26 days ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

joffinjoy/o-horizon-recommendation

Reference repository for the O-Horizon Recommendation Engine featured in Neo4j Graphversation Episode 2

Language: JavaScript - Size: 15 MB - Last synced at: 3 months ago - Pushed at: about 2 years ago - Stars: 4 - Forks: 2

Sourabh-Kumar04/Raso-movie-recommendation

Raso Movie Recommendation System is an AI-powered web application that suggests movies based on user preferences, genres, ratings, and similarity scores. It leverages machine learning techniques to enhance recommendations.

Language: Jupyter Notebook - Size: 8.67 MB - Last synced at: about 2 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

prince7711sharma/Movie-Recommender

Movie Recommendation System This repository contains a Python-based Movie Recommendation System that leverages Machine Learning and Vectorization methods such as TF-IDF to recommend movies based on content similarity. The system processes datasets containing movie information like titles, genres, and descriptions to generate accurate and efficient

Language: Jupyter Notebook - Size: 8.67 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

SciddhantoSinha/Book-Recommender-System-Using-Collaborative-Filtering

This project implements a book recommendation system using machine learning techniques. It helps users find books based on preferences, book ratings, and similarity measures.

Language: Jupyter Notebook - Size: 17 MB - Last synced at: 3 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

Juwono136/book-recommendation-system-using-content-based-filtering-and-collaborative-filtering

Book recommendation system using model development with Content-Based Filtering and Collaborative Filtering techniques.

Language: Jupyter Notebook - Size: 24 MB - Last synced at: 3 months ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 2

gargibendale/MyntraFit

Language: Jupyter Notebook - Size: 8.22 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

raquelanamb/harmonaic

A music recommendation assistant.

Language: Python - Size: 26.8 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 1

anwesha0123/Unsupervised_Learning_Practice

This repository contains a collection of unsupervised learning projects leveraging machine learning techniques such as clustering, dimensionality reduction, and anomaly detection. These projects are designed to extract meaningful insights from unlabeled data using algorithms like K-Means, DBSCAN, PCA, Autoencoders, and more.

Language: Jupyter Notebook - Size: 0 Bytes - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

guidasneves/spotify_recsys

End-to-end Spotify recommendation system

Language: Jupyter Notebook - Size: 4.94 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

karenwky/Recommendation_System_Allrecipes

recommending recipes with content-based filtering approach

Language: Jupyter Notebook - Size: 8.58 MB - Last synced at: 3 months ago - Pushed at: 10 months ago - Stars: 7 - Forks: 3

hk-kumawat/Movie-Recommender-System

🎬 It helps you discover films. Search for your favorite movies, get a "Surprise Me" pick, and explore trending movies—all while viewing live details like posters, trailers, ratings, and cast information.

Language: Jupyter Notebook - Size: 15.6 MB - Last synced at: 3 months ago - Pushed at: 4 months ago - Stars: 2 - Forks: 0

yash1th-yerra/Hybrid-Model-Movie-Recommendation-System

This is an extension project to Movie-Recommendation-System https://github.com/yash1th-yerra/Movie-Recommendation-System.

Language: Jupyter Notebook - Size: 1.01 MB - Last synced at: about 1 month ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

vsancnaj/Hybrid-Recommendation-System

A hybrid recommendation system combining Item-Based Collaborative Filtering and Content-Based Filtering to suggest skincare products based on user preferences, product ingredients, and ratings. Features a Flask API and an interactive Streamlit Web App for personalized recommendations.

Language: Jupyter Notebook - Size: 20.5 KB - Last synced at: 3 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

razamehar/Movie_Recommendation_System

This project employs multiple recommendation techniques, including popularity-based ranking with Bayesian averages, content-based suggestions using cosine similarity, and collaborative filtering via the Surprise library.

Language: Python - Size: 14.2 MB - Last synced at: 3 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

Rohansoni45/movie-recommendation-system

This project is a Content-Based Recommender System that suggests movies to users based on their preferences and watched history. The system leverages cosine similarity to find and recommend movies similar to a selected title. It is built using Python and libraries like Pandas, NumPy, and Scikit-learn.

Language: Python - Size: 2.06 MB - Last synced at: 3 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

soumyajit4419/Movie_Recommender_System

Recommending movies to user using various Colaborative Filtering and Content Based Filtering.

Language: Jupyter Notebook - Size: 3.12 MB - Last synced at: about 2 months ago - Pushed at: over 5 years ago - Stars: 7 - Forks: 1

bimarakajati/Anime-Recommender-System

This project analyzes anime recommendation data using two approaches: Content-Based Filtering with K-Nearest Neighbors (KNN) and Collaborative Filtering with the RecommenderNet model. Data is taken from the Anime Recommendations Database, and various techniques such as TF-IDF and Early Stopping are used to improve model accuracy.

Language: Jupyter Notebook - Size: 4.23 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

abhipatel35/MovieMatcher-Movie-Recommender-System

A robust movie recommendation system using the MovieLens dataset, employing Collaborative Filtering, Matrix Factorization, and Hybrid Models to enhance recommendation accuracy and diversity.

Language: Jupyter Notebook - Size: 776 KB - Last synced at: 4 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

DragoKami/Anime-Recommendation-System-Amime2022-dataset

Anime Recommendation System for websites with EDA

Language: Jupyter Notebook - Size: 897 KB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

Aksoni07/Movie-Recommendation

A hybrid movie recommendation system designed to deliver personalized and accurate suggestions by combining user preferences, item attributes, and collaborative patterns, ensuring a seamless and engaging experience.

Language: Jupyter Notebook - Size: 1.16 MB - Last synced at: about 1 month ago - Pushed at: 6 months ago - Stars: 0 - Forks: 1

Upasanadhameliya/Django-Movie-Recommendor

Movie Website built on python Django framework; Uses Content Based Predictive Model approach to predict similar movies based on the contents/genres similarities

Language: Python - Size: 62.8 MB - Last synced at: 3 months ago - Pushed at: almost 5 years ago - Stars: 14 - Forks: 5

ChoukriLach/Movie-Recommendation-Systems

Build movie recommender systems with a collaborative filtering approach and a content-based deep learning method

Language: Jupyter Notebook - Size: 3.07 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 1 - Forks: 0

AxelSeanCP/Anime-Recommender-System

A Project from Dicoding's Machine Learning Expert Class. Recommender System for Anime using Content-Based Filtering and Collaborative Filtering

Language: Jupyter Notebook - Size: 776 KB - Last synced at: 3 months ago - Pushed at: 8 months ago - Stars: 2 - Forks: 0

gerinsp/Movie-Recommendation-System

Developed a Movie Recommendation System using content-based filtering and collaborative filtering to suggest personalized movie recommendations.

Language: Jupyter Notebook - Size: 299 KB - Last synced at: about 7 hours ago - Pushed at: 7 months ago - Stars: 1 - Forks: 0

jnopareboateng/Lhydra_rs

Journey detailing Lhydra Hybrid Music Recommender System

Language: Jupyter Notebook - Size: 379 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

easonlai/content_based_product_recommendation_samples

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.

Language: HTML - Size: 118 KB - Last synced at: 4 months ago - Pushed at: about 3 years ago - Stars: 2 - Forks: 1

Yukta026/book_recommender_system

I have created a book recommender system that recommends similar books to the reader based on his/her interest. This project shows results of collaborative and content-based filtering of the given dataset.

Language: Jupyter Notebook - Size: 39.9 MB - Last synced at: 4 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

Nourine-Nadir/Movie-Recommander-system-Content-based-

A Movie Recommender System implemented in Python using content-based filtering techniques. The system processes a dataset of 10,000 movies, extracting features such as keywords, genres, cast, and crew information to generate movie recommendations based on user input.

Language: Jupyter Notebook - Size: 2.98 MB - Last synced at: 4 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0

codeasarjun/MovieMingle

Movie Recommendation System is a web application designed to provide personalized movie recommendations to users based on their input movie titles.

Language: Python - Size: 75.1 MB - Last synced at: 3 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

yajasarora/Movie-Recommendation-Model-using-NLP-Techniques

A content-based movie recommendation model using NLP techniques to analyze and suggest movies based on metadata like genres, keywords, and plot summaries.

Language: Jupyter Notebook - Size: 8.74 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 0

Aaryan015/AI-book-recommender-system

Hybrid recommender system - Collaborative filtering + Content based filtering (same as used by Netflix).

Language: Jupyter Notebook - Size: 6.16 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

amalaluan/wa_mern_project-peso

A MERN stack-based automated job portal integrating a content-based algorithm to match candidates with relevant job opportunities. Features include user registration, job listing management, resume submission, and job application tracking.

Size: 2.93 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

AhmedShoeb0/MovieRecommenderSystem

A simple movie recommender system that uses two main approaches to make recommendations: Content-based algorithm and Collaborative filtering algorithm (User-based).

Language: Python - Size: 14.3 MB - Last synced at: 11 months ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

farhanazmiCS/triprecco-public

Don't know where to go next? Let Triprecco for you!

Language: Python - Size: 1.32 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

Maoelan/indo-tour-recommend

Dicoding Submission Machine Learning Terapan

Language: Jupyter Notebook - Size: 454 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 2 - Forks: 0

sharmeen-k/Movie-Recommender-System

A content-based system recommends movies to an input user based on the weighted genre score of a movie and a collaborative filtering system recommends movies liked by other users having a similar taste profile as the input user.

Language: Jupyter Notebook - Size: 39.1 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

jss415/recommendation-system

Evaluation of Recommendation Systems

Language: Jupyter Notebook - Size: 1.05 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

Nathius262/trend-fitness-recommendation-technique

Trend Fitness is a web application dedicated to providing professional fitness advice which will include a range from fitness plans to diet plans catered to every individual needs. I believe that my web application will embark on a transformative journey towards a healthier lifestyle.

Language: Jupyter Notebook - Size: 586 KB - Last synced at: 4 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 1

jmarihawkins/neural-network-challenge-1

The purpose of this project is to predict student loan repayment success using a neural network. Neural networks are computational models inspired by the human brain's structure and function, consisting of layers of interconnected nodes or "neurons" that can learn to recognize patterns in data.

Language: Jupyter Notebook - Size: 36.1 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

heddgehogg/gamerecomsystem

The purpose of this project is to develop a recommender system based on content-based filtering in the Python programming language.

Language: Jupyter Notebook - Size: 68.5 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

convenience-tinashe-chibatamoto/Movie-Recommendation-System

A movie recommendation system using IMDb's weighted ratings and custom filters.

Language: Jupyter Notebook - Size: 112 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

saivikas1/Music-Recommender-System

Language: Python - Size: 29.1 MB - Last synced at: 5 months ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

Manu-Abuya/Music-Recommendation-System

Build a personalized Music Recommendation System using Spotify API and Python. The system uses content-based and hybrid filtering to suggest songs based on user preferences, enhancing the music discovery experience.

Language: Jupyter Notebook - Size: 17.6 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

adrianmarino/thesis-paper

Collaborative and hybrid recommendation systems

Language: Jupyter Notebook - Size: 353 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 1

jtonglet/Recommender-Systems-Polimi

Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.

Language: Python - Size: 16.7 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 1

Ayushma00/Text-Mining-and-Recommendation-System-for-Yelp-Dataset-

Restaurant Recommendation Application

Language: Jupyter Notebook - Size: 11.9 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 1

shadowbourne/CACBCF-Recommender-System

3rd Year: 1st - 92. A Novel Context Aware Restaurant Recommender System Using Content-Boosted Collaborative Filtering (CACBCF).

Language: Python - Size: 43.1 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 1

DDILLOUD/Spotify-AI-Music-Recommender

AI Music Rec w/ Spotify & Streamlit

Language: Python - Size: 0 Bytes - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

mariodmtrv/music-recommendations

Music recommender system with collaborative and content-based filtering

Language: Python - Size: 22.5 KB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 0 - Forks: 1

TommyNiemi/RECOMMENDER_SYSTEM

A movie recommender. Collaborative and content based filtering hybrid model.

Language: HTML - Size: 1.61 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

vishant-mehta/Music-Recommendation-System-using-Cosine-Similarity

The recommender framework goes about as a friend in need and channels the melodies that are reasonable for that client at that point. It likewise expands the client's fulfilment by playing fitting tune at the correct time, and, in the interim, limit the client's work.

Language: Jupyter Notebook - Size: 12.6 MB - Last synced at: about 1 year ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0