IEEE

Collaborative Filtering Item Recommendation Methods based on Matrix Factorization and Clustering Approaches

Abstract: This paper studies various collaborative filtering item recommendation methods based on matrix factorization and clustering approaches. We develop six methods that are modified based on ...
IEEE

An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems

Abstract: Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ...
Nature

lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering

In recent years, with the continuous development and innovation of high-throughput biotechnology, more and more evidence show that lncRNA plays an essential role in biological life activities and is ...
GitHub

A comprehensive restaurant recommendation system that combines multiple recommendation approaches including content-based filtering, collaborative filtering, matrix ...

restaurant_recommendation_system/ ├── src/ │ ├── data/ │ │ ├── generate_data.py # Data generation utilities │ │ └── data_loader.py # Data loading and preprocessing │ ├── models/ │ │ ├── ...
GitHub

shahriar-rahman/Neural-Collaborative-Filtering

This is our implementation for the paper: Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua (2017). Neural Collaborative Filtering. In Proceedings of WWW '17, Perth, ...
Nature

Deep learning-based multi-criteria recommender system for technology-enhanced learning

Recommender systems (RSs) have evolved significantly since their early applications in the mid-1990s. This was driven by the need to filter vast amounts of digital content and tailor suggestions to ...