Communication-Efficient Stratified Stochastic Gradient Descent for Distributed Matrix Completion
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Updated
May 8, 2023 - C
Communication-Efficient Stratified Stochastic Gradient Descent for Distributed Matrix Completion
DST approach on Recommended Systems(RS).
An ontology proposed for annotates semantically the data of a Twitter user, for instance: tweets; profile.
Implementation of faster Bayesian Optimization method for Matrix Factorization models hyperparameters and experiments with it.
Implementation of the paper - Neural Collaborative Filtering
GitSum is a novel approach to the summarization of README.MD, it helps automatically fill the blank “About” field for GitHub repositories. It is built on top of BART and T5, fine-tuning on existing data to perform recommendations for repositories with a missing description.
A one-stop site for discovering and getting recommendations on Movies & TV Shows.
This repository is for study recommender systems.
A movie recommendation engine
Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback. I have applied basic content-based recommendation system using python.
This repository explores the use of tensorrec library in python to make a recommendation system for anime.
Implementation of model-based and memory-based collaborative filtering to predict movie rating
Challenge for the "Recommender Systems" course at Politecnico di Milano - AY 2023/2024
Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay
This repository provides a reference implementation of gOCCF: Graph-Theoretic One-Class Collaborative Filtering Based on Uninteresting Items (AAAI 2018).
Code for the paper "Effective Contact Recommendation in Social Networks by Adaptation of Information Retrieval Models"
This repository houses 3 different Jupyter Notebooks that each analyze the similarity in data points to most effectively inform customer recommendations in the retail space.
My graduation project for Computer Engineering Department.
Book recommender with Python
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