Loading repository data…
papacosmas / repository
The purpose of this R project is to create a **rating recommender system through machine learning training** That recommender system will be able to predict a users rating into a new movie. For training and testing our ML models, we will use the 10M (millions) row rating dataset named MovieLens created by the University of Minnesota. It was released at 1/2009 so our newest movies are until 2008. In order to find a pattern and behavior of the data, the data sets where enhanced by many new features (dimensions). As validation of the models we wil use RMSE. During the project are given more explanations. Many algorithms and ML models where used in order to achieve the lowest RMSE. Such us: **Matrix Factorization with parallel stochastic gradient descent, H2o stacked ensembles of (GBM,GLM,DRF,NN). Also they where used H2o Auto ML models** More details are below and also during the project. In case you dont want to wait and train the models, you can download them from my github and load them. There are 2 types of recommender systems: **Content filtering (based on the description of the item also called meta data or side information)** And **collaborative Filtering**: Those techniques are calculating the similarity measures of the target ITEMS and finding the minimum (Euclidean distance, or Cosine distance, or other metric, it depends on the algorithm). This is done by filtering the interests of a user, by collecting preferences from many users (collaborating). The underlying assumption is that if a person X has the same opinion as a person Y then the recommendation system should be based on preferences of person Y (similarity). We will enhance the collaborative filtering with the help of **Matrix factorization**. MF is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by **decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices**. This family of methods became **widely known during the Netflix prize challenge due to its effectiveness as reported by Simon Funk in his 2006 blog post**, where he shared his findings with the research community LINK (https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems) We will apply **Matrix Factorization with parallel stochastic gradient descent**. With the help of "recosystem" package it is an R wrapper of the LIBMF library which creates a Recommender System by Using Parallel Matrix Factorization. The main task of recommender system is to predict unknown entries in the rating matrix based on observed values. The main purpose is to calculate the matrix RMXn by the product of the two matrixes of the lower dimension, Pkxm and Qkxn : RQ More info on the recosystem package and the techniques LINK (https://cran.r-project.org/web/packages/recosystem/vignettes/introduction.html)
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
The purpose of this R project is to create a rating recommender system through machine learning training That recommender system will be able to predict a users rating into a new movie. For training and testing our ML models, we will use the 10M (millions) row rating dataset named MovieLens created by the University of Minnesota. It was released at 1/2009 so our newest movies are until 2008. In order to find a pattern and behavior of the data, the data sets where enhanced by many new features (dimensions). As validation of the models we wil use RMSE. During the project are given more explanations. Many algorithms and ML models where used in order to achieve the lowest RMSE. Such us: Matrix Factorization with parallel stochastic gradient descent, H2o stacked ensembles of (GBM,GLM,DRF,NN). Also they where used H2o Auto ML models More details are below and also during the project. In case you dont want to wait and train the models, you can download them from my github and load them. There are 2 types of recommender systems: And : Those techniques are calculating the similarity measures of the target ITEMS and finding the minimum (Euclidean distance, or Cosine distance, or other metric, it depends on the algorithm). This is done by filtering the interests of a user, by collecting preferences from many users (collaborating). The underlying assumption is that if a person X has the same opinion as a person Y then the recommendation system should be based on preferences of person Y (similarity). We will enhance the collaborative filtering with the help of . MF is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by . This family of methods became , where he shared his findings with the research community LINK ( We will apply . With the help of "recosystem" package it is an R wrapper of the LIBMF library which creates a Recommender System by Using Parallel Matrix Factorization. The main task of recommender system is to predict unknown entries in the rating matrix based on observed values. The main purpose is to calculate the matrix RMXn by the product of the two matrixes of the lower dimension, Pkxm and Qkxn : RQ More info on the recosystem package and the techniques LINK ()
Selected from shared topics, language and repository description—not editorial ratings.
NxRLab /
Modern Robotics: Mechanics, Planning, and Control Code Library --- The primary purpose of the provided software is to be easy to read and educational, reinforcing the concepts in the book. The code is optimized neither for efficiency nor robustness.
89/100 healthrholder /
Retrying is an Apache 2.0 licensed general-purpose retrying library, written in Python, to simplify the task of adding retry behavior to just about anything.
81/100 healthsmgorelik /
The exploit samples database is a repository for **RCE** (remote code execution) exploits and Proof-of-Concepts for **WINDOWS**, the samples are uploaded for education purposes for red and blue teams.
79/100 healthLe0nX /
Modern Robotics: Mechanics, Planning, and Control C++ Library --- The primary purpose of the provided software is to be easy to read and educational, reinforcing the concepts in the book. The code is optimized neither for efficiency nor robustness. http://modernrobotics.org/
76/100 healthcsells /
The purpose of the go_router for Flutter is to use declarative routes to reduce complexity, regardless of the platform you're targeting (mobile, web, desktop), handling deep linking from Android, iOS and the web while still allowing an easy-to-use developer experience.
22/100 healthaccel-brain /
The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.
87/100 health