svdpp

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yudi-mars / daisyRec

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)

afm slim pytorch collaborative-filtering matrix-factorization vae recommender-system factorization-machines ease k-nearest-neighbors item2vec deepfm neural-collaborative-filtering neumf cdae nfm svdpp biasmf

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Python

sisinflab / elliot

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Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

recommendations machine-learning slim keras collaborative-filtering matrix-factorization knowledge-graph vae recommender-system k-nearest-neighbours k-nn content-based-recommendation funksvd deepfm bprmf neural-collaborative-filtering nfm tensorflow2 svdpp

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Python

👁 Tf-Rec

Praful932 / Tf-Rec

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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.

python machine-learning deep-learning tensorflow python-library machine-learning-algorithms keras matrix-factorization recommendation-system recommender-system svd svdplusplus svdpp

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Python

aliceagrawal / HM-Recommender-System-App

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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.

python modeling jupyter-notebook python3 python-3 cosine-similarity svd non-negative-matrix-factorization surprise-python rfm-analysis svdpp

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Jupyter Notebook

Lednik7 / RuCode3

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Применяем Singular Value Decomposition with implicit feedback для задачи рекомендации аниме

python mipt svd annoy catboost svd-matrix-factorisation svdpp tf-recsys rucode

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Jupyter Notebook

Balajirvp / Recommender-Systems---Content-Based-Systems-and-Collaborative-Filtering

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

collaborative-filtering recommendation-system recsys recommender-system content-based-recommendation user-item-utility-matrix user-based-recommendation svdpp item-based-recommendation surprise-library sbert-implementation

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Jupyter Notebook

stxupengyu / Yelp-Recomendation-Algorithms

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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).

svd nmf yelp-dataset pmf svdpp surprise-library

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Jupyter Notebook

stxupengyu / MF-for-Movie-Recommendation

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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.

svd svdpp surprise-library

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Jupyter Notebook

xianchen2 / Recommender_System

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A Python implementation of Collaborative Filtering algorithms used in Recommender Systems

matrix-factorization recommender-system svd svdpp

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Jupyter Notebook

Magus4450 / Movie-Recommendation-System-using-Collaborative-Filtering

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

collaborative-filtering svd surprise fastapi svdpp

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Python

somjit101 / Netflix-Movie-Recommendation

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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.