Multi-omics Autoencoder Integration: Deep learning-based heterogenous data analysis toolkit

Anime recommender system using collaborative filtering and latent factor model

Hierarchical Model of Species Communities (HMSC) is an R package, which provides extensive set of analytical tools devised for numerical analysis of data on ecological communities or other similarly structured data. The methodological core of the HMSC approach is centered on combination of hierarchical regression and latent factor models.

Chaos - a first of its kind framework for researching Reciprocal Recommender Systems (RRS).

Google Local Rating Prediction using Latent Factor Model. Recommender System - CSE 258 Assignment 1

Multivariate time series Vector Autoregression Model (VAR) on real world GDP and DPI (and some other indexes). Bayesian Structured Time Series (BSTS).

Code for UCSD CSE 258 Web Mining and Recommender Systems

# This Repository contains implementation of different Recommendation Engine Algorithms

Implementation of various recommender systems, for course CS F469 Information Retrieval

Correction of batch effects in DNA methylation data

implemented model and refined with another model which stands 3rd in the contest of 1million$ prize worth, that aims to decrease original rmse by 10 percent with efficient time-complexity.

A Tensorflow implementation of Factorization Machines

A movie recommend app using React-native and integrated AI recommender system: LFCF-DL and Content-Based Filtering.

Recommender system with Netflix database using matrix factorization

An implementation for our paper: Time sensitivity-based popularity prediction for online promotion on Twitter (Information Sciences, 2020). This is an extended Latent Factor Model that can predict popularity values of tweets on Twitter when they are published at various times. The data includes Twitter user profiles and tweet information (tweetI…

Recommender System wrapped with a Binary Classifier

Correction of batch effects with BEclear as a command line tool

Implementing different approaches for recommendation systems