Relations / rating prediction in trust-based social networks
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Updated
Mar 29, 2017 - R
Relations / rating prediction in trust-based social networks
A Python Package for Benchmarking Collaborative Filtering Algorithms in Recommendation
An Amazon product recommender system that predicts product ratings and review helpfulness based on linear regression and latent-fact model.
Google Local Rating Prediction using Latent Factor Model. Recommender System - CSE 258 Assignment 1
for beginners tutorial
Movie ratings prediction
Worked on building a predictive model by considering multi collinearity and applying regression technique as well as other machine learning concepts related to factors or variables using SAS programming.
Worked on building a predictive model by considering multicollinearity and other Machine learning concepts related to factors or variables using R programming.
To predict if a customer will like a movie or not depending on his past ratings and preferences using RBMs.
Elo Rating System written in Swift for Swift Package Manager
The goal of this project was to predict reviews' star ratings on Yelp using the review text. We built the following models that perform text analysis on review data to predict the rating stars.
Machine learning ------- rating prediction for the review of commenting restaurant
Movie Revenue & Ratings Prediction Using 5000 IMDB Movies [Python, Machine Learning, GitHub]
Predicting Amazon ratings based on reviews by Text Classification using the Naive Bayes Algorithm.
This project is an implementation of simple rating prediction systems for items from user
[Python3.6] IEEE Paper "Matrix Factorization Techniques for Recommender Systems" by Koren,Bell,Volinsky
Movie Recommendation Using Matrix Factorization.
Implemented a model that is capable of predicting a restaurant rating taking into account several factors such as reviews and restaurant facilities. Analysis of review is done based on NLP techniques that include polarity analysis, TF-IDF which are all followed by pre-processing.
Implemented a model that is capable of predicting a restaurant rating taking into account several factors such as reviews and restaurant facilities. Analysis of review is done based on NLP techniques that include polarity analysis, TF-IDF which are all followed by pre-processing.
Predict the rating that a user will give to a book given their past book ratings.
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