A Python implementation of Collaborative Filtering algorithms used in Recommender Systems

Movie Recommendation system with content-based and collaborative filtering

Linear regression, retraining models, regularization, SVC, RandomForest, K-means, PCA, Recommender systems, Neural Network, Keras

Python Recommendation System

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.

Collaborative Filtering based movie recommendation that uses matrix factorization to generate rating predictions for user-movie,

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).

surprise svd

Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.

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.

Применяем Singular Value Decomposition with implicit feedback для задачи рекомендации аниме

Tf-Rec is a python💻 package for building⚒ Recommender Systems. It is built on top of Keras and Tensorflow 2 to utilize GPU Acceleration during training.

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.

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)