Topic: surprise-library Goto Github
Some thing interesting about surprise-library
Some thing interesting about surprise-library
surprise-library,Recommendation engine in Surprise that populates movie recommendations for users based on their existing preferences.
User: adinas94
surprise-library,Using a dataset from MovieLens, a movie recommendation system was created that recommends to users which movies they will like. The system also goes a step further to solve the cold start problem, which is when there is a new user in the dataset and there is no prior information on them. This system also finds a solution to this.
User: ahing
surprise-library,Machine Learning - Recommendation System
User: akashbangalkar
surprise-library,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-library,A book recommendation system using model based collabritive filtering. It is based on SVD machine learning model. It generate top 10 recommendation of books.Here i used surprise library.
User: auniyal486
surprise-library,Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.
User: balajirvp
surprise-library,Système de recommandation
User: bilnab
surprise-library,Simple Recommender Systems
User: deekshith126
surprise-library,Tasty Trail: Restaurant Recommendation System
User: ebeui
surprise-library,This Repository provides the basic code snippets for all the most widely used ML Algorithms like Supervised, Unsupervised, and Recommender system algorithms.
User: enockjamin01
Home Page: https://colab.research.google.com/drive/1oQ-0kTxtxyu-nLXFdTlyvH6BcQbfqEl6?usp=sharing
surprise-library,The project used Python to create a personalized book recommendation system that analyzed users' past ratings on books to identify their preferences and patterns and suggested books that the user is likely to enjoy but has not read yet.
User: fridahkimathi
surprise-library,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-library,To recommend the next 10 movies to the user using the Prized Dataset provided by Netflix - over the span of 10 days for Capstone Project.
User: hilarylim
surprise-library,A Book Recommender System: Collaborative Filtering using Surprise (k-NN Baseline model)
User: izlata
surprise-library,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-library,영화 추천 시스템
User: kimjinho1
Home Page: https://www.kaggle.com/rounakbanik/the-movies-dataset
surprise-library,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-library,I built recommender systems for recommending products to user using Model-based recommendation system.
User: luthfiraditya
surprise-library,Build a movies recommendation system clone using Movielens dataset to construct recommendation system such as Simple recommender, Content based recommender (based on movie description and metadata) , Collaborative-Filtering based recommender , and a Hybrid recommender system.
User: mani24singh
surprise-library,Predict user rating for a netflix movie.
User: manish-vi
surprise-library,Did you ever wonder how the recommendations on Netflix work? Find out in this project, where I build three basic movie recommenders and implement them in a streamlit App.
User: moritzbaumann
surprise-library,Predicted missing ratings using SVD algorithm from the Surprise Library for items from a file containing user ratings for multiple items by comparing a user’s ratings for available items with those of other user’s ratings and the project was built in Python
User: nehal-pawar
surprise-library,Built a movie recommender system using Movielens dataset using both content-based filtering approach and collaborative filtering method.
User: nnvij
surprise-library,Implementation for two different types of recommendation systems (Content-based and collaborative filtering)
User: osamaalhalabi
surprise-library,I created a recommender system using a Python scikit named Surprise. The purpose of building this system is to predict a person's preferences so the user can find what they are looking for faster.
User: peteremiller
surprise-library,Recommendation Systems tutorial
User: romario076
surprise-library,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-library,Deployed Product Recommendation Model using collaborative filtering.
User: sajalsinha
surprise-library,
User: satrapankti
surprise-library,A Movie Recommendation System using Collabrative Filtering
User: shulavkarki
surprise-library,A case study of the Netflix Prize solution where, given anonymous data of users and the ratings given to movies, the objective to provide recommendations to users for movies which they would like, based on their past activity and taste.
User: somjit101
surprise-library,🔮Trying to find the best movie to watch on Netflix can be a daunting. Case Study for Recommendation System of movies in Netflix.🔧
User: storieswithsiva
Home Page: https://iamsivab.github.io
surprise-library,使用矩阵分解方法进行电影推荐的评分预测。The matrix factorization method is used to predict the rating of movie recommendation.
User: stxupengyu
surprise-library,在Yelp数据集上摘取部分评分数据进行多种推荐算法(SVD,SVDPP,PMF,NMF)的性能对比。Some rating data are extracted from yelp dataset to compare the performance of various recommendation algorithms(SVD,SVDPP,PMF,NMF).
User: stxupengyu
surprise-library,Movie recommendation system to find common movie interests among a group of people.
User: sumanthvrao
surprise-library,Recommendation_Systems
User: sureshathanti
surprise-library,A movie recommender application
User: thimyxuan
surprise-library,Proyectos de Data Science y Machine Learning.
User: viviancaro
Home Page: https://viviancaro.github.io/Data-Science/
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