This repository contains the machine learning part of the project especially the used algorithms for the recommendation system
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Updated
May 28, 2023 - Jupyter Notebook
This repository contains the machine learning part of the project especially the used algorithms for the recommendation system
The "Music Recommender System using Spotify API" project aims to create a personalized music recommendation system for users based on their listening preferences and behavior. By leveraging the Spotify API, we can access a vast collection of music data, including tracks, artists, genres, and user playlists.
This is an app for collaborative and hybrid filtering using multiple csv data a model is trained and a flask is used for the web representation of model
Repository of the python scripts for the CS competition held in Kaggle obtaining the 4th place
The project is based on a Hybrid recommendation engine that uses both Collaborative as well as Content based filtering methods to suggest streamers to the online users based on the type content they consume.
Explore the Hybrid Recommender System on E-commerce Data repository! This GitHub project showcases a solution for building a hybrid recommender system. Dive into the code, discover innovative approaches, and enhance your understanding of creating effective recommendation systems tailored for E-commerce Data.
Recommendation Systems with mathematical modelling
A movie recommender application
Content, Collaborative, and Hybrid Movie recommendation system
Lhydra Hybrid Music Recommender System
Comparison of two approaches for building a recommender system presented. The first one is a collaborative filtering. The second one is hybrid recommender system. This project is the second stage of a contest for an internship in VK.
USC DSCI 553 - Foundations & Applications of Data Mining - Spring 2024 - Prof. Wei-Min Shen
Recommender system challenge @ Polimi
Recommender System 2019 Challenge PoliMi
This repository houses the codebase for a Book Recommendation System, crafted using collaborative filtering, Flask, and cosine similarity. The system employs advanced machine learning techniques to generate personalized book recommendations based on user preferences.
2021년 경상북도 데이터 경진대회 | 추천 알고리즘을 이용한 맞춤형 식품 추천 서비스
Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.
🎓 Final Project for Completing Bachelor Degree in Petra Christian University. Create Hybrid Recommender System for Interior Products and its Services using Data Implicit Feedback
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