Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
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Updated
Dec 10, 2025 - Python
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
This application focuses on recommending music artists using NCF. The dataset, HetRec 2011 Last.fm, includes user-artist interactions, friend relationships, and tagging information for 1,892 users and 17,632 artists. The model predicts top artist recommendations for users, incorporating social and listening behavior data to enhance personalization.
A collection of Movie Recommender System models developed using PyTorch
Anime Recommender System with various recommender system algorithms implemented in python
A Deep Learning Based Context-Aware Recommendation Library
This project it's a movie recommendation engine composed of a custom NCF model to predict user preferences and generate personalized recommendations for large-scale user-item interaction data.
A Movie Recommendator built using Neural Collaborative Filtering
Book recommendation system and reader rating analysis.
SER for emotions recognition - CNN & NCF for recommendations
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)
Neural collaborative filtering recommendation system on Movie lens 100k dataset
Factorization Machine models in PyTorch
A Julia implementation of three different recommender systems based on the concept of Neural Collaborative Filtering.
A collection of diverse recommendation system projects, spanning collaborative filtering, content-based methods, and hybrid approaches.
implementation of federated neural collaborative filtering algorithm
PyTorch Implemenation for Neural Graph Collaborative Filtering
Neural recommendation models in Python, using Tensorflow 2.0 & Keras.
Restaurant Recommender System: Neural Collaborative Filtering
Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.
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