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Some thing interesting about surprise-python
Some thing interesting about surprise-python
surprise-python,This app analyzes ratings to suggest ideal products for e-commerce platforms. Upload your data, explore user trends, and train a model to predict what your customers will love!
User: adarsh79
surprise-python,This is a recommendation system based on Singe value Decomposition method ( Collaborative filters )
User: akshatjain1999
surprise-python,Built a collaborative filtering and content-based recommendation/recommender system specific to H&M using the Surprise library and cosine similarity to generate similarity and distance-based recommendations.
User: aliceagrawal
surprise-python,This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.
User: amanjeetsahu
surprise-python,Machine Learning homework project at EPFL
User: andreamanzini
surprise-python,A movie recommendation engine made with FastAPI and Surprise (under the 100k movielens dataset)
User: apfel-das
surprise-python,Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity
User: artisan1218
surprise-python,This program combines several recommendation approaches in order to predict and display to users recommendations of hotels located in the Paris area.
User: azury74
surprise-python,Adaptive Systems project assignment at Universidad Politécnica de Madrid: Creating a basic Recommender System for movies with Python and Surprise library
User: beatrice-trinidad
surprise-python,Código-fonte desenvolvido para implementação da parte prática referente dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Ciência da Computação.
User: brendasalenave
surprise-python,Implementation of the model iGSLR
User: camcochet
surprise-python,An overview of reccomendation systems in Python
User: dacker15
surprise-python,The Hybrid Movie Recommender is a system that recommends movies using a combination of collaborative and content-based filtering techniques. The system is designed to address the cold start problem(new users) by using a popularity based approach. The dataset used for the system is obtained from Kaggle.
User: danielchristopher513
surprise-python,The goal of this project is to develop recommendation systems for amazon reviews dataset using Surprise package. This project demonstrated the application of 6 recommendation systems, as well as the preprocessing steps needed to apply the methods.
User: didizhx
surprise-python,Recommendation Systems
User: edohgoka
surprise-python,This repository contains the source code and documentation for a Bachelor's thesis project that explores two different approaches to developing a movie recommendation system.
User: emanuelneziraj
surprise-python,🛍️ Amazon Recommender Study 🚀 A Python exploration into machine learning for e-commerce personalization, using Amazon's Electronics data. Investigates algorithms like SVD, KNNBaseline for predicting user preferences, offering insights into future shopping enhancements
User: farzanmrz
surprise-python,Collaborative , Contents Filtering Recommdar System
User: forestinblue
surprise-python,Phase 4 project of the Flat Iron curriculum of Data Science in Moringa School
User: fredrickkyeki
Home Page: https://movies-like-x.onrender.com/
surprise-python,
User: fujun0406
surprise-python,Use the Scikit-Network for PageRank algorithms including Topic-specific PR and improve the performance of various recommendation-systems using Surprise library
User: giulio-derasmo
surprise-python,
User: gustavodinizmonteiro
surprise-python,Recommender system that applies a user-to-user collaborative filtering algorithm on the MAL dataset to recommend anime for users.
User: hmc-cs-azhao
surprise-python,Using the MovieLens dataset with Surprise to compare different algorithms for rating prediction, and also create a movie recommendation system on top of it.
User: jacobceles
surprise-python,Suprise-Python Wrapper for Persa.jl
Organization: juliarecsys
surprise-python,This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.
User: klaudia-nazarko
surprise-python,Repository to demonstrate how to use machine learning to generate recommendations
User: luis-palacios
surprise-python,Use of Surprise Package in Python for Recommender System
User: mdanish99
surprise-python,Grocery Recommendation on Instacart Data
User: melodygr
surprise-python,MovieLens recommended system project
User: mldk-tech
surprise-python,Recommender system with Netflix database using matrix factorization
User: mohamedmansoura
surprise-python,Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
User: prakruti-joshi
surprise-python,Projeto Final de Graduação - Engenharia de Computação UNICAMP 2019. Revisão de Ténicas em Sistemas de Recomendação.
User: raissaccorreia
surprise-python,A basic movie recommendation system using collaborative filtering methods on MoiveLens dataset.
User: rajtulluri
surprise-python,Recommendation Systems tutorial
User: romario076
surprise-python,This repository covers a project of creating a recommendation system using collaborative filtering on the Grouplens movielens database. The surprise library is utilized to test out different models (KNN Basic, KNN Baseline, and SVD). SVD was found to be the most accurate and then was implemented into the system. The cold start problem was addressed by giving new users the opportunity to rate a random sample of 5 movies from movies that are among the most popular.
User: roweyerboat
Home Page: https://roweyerboat.github.io/the_helpful_library_of_surprise
surprise-python,Getting a better grasp of recommender systems
User: ruxandraburtica
surprise-python,Building a Recommendation engine course walkthrough. IDE used :- Spyder ; Environment name :- RecSys (created in Anaconda Navigator) ; Python Package used :- Surprise ; Tutor :- Frank Kane, Sundog Education
User: saket-sk
surprise-python,A generalized items recommender that lists out top 5 recommendations based on users with similar choices.
User: sarthak71
surprise-python,This Project is a simplifed Movie Recommendation System
User: shaina-12
surprise-python,Exploring Recommender Systems using various Machine Learning Models like scikit-learn, Surprise, NLP and collaborative filtering using KNN and Tensorflow.
User: sheetalbongale
surprise-python,Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering
User: singhsidhukuldeep
surprise-python,A Collaborative filtering recommendation system
User: thislawyercodes
surprise-python,기본적인 Recommendation System을 갖춘 REST API 서버와, 이를 표현하기 위한 간단한 프론트엔드를 구현한 페이지입니다.
User: timbergrizz
surprise-python,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.
User: unclebrod
surprise-python,Workshop Surprise para a disciplina MATE65 (UFBA)
User: victormartinez
surprise-python,A simple Product Recommendation System.
User: vignesh010101
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