rating-prediction

An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.

Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems

pyRecLab is a library for quickly testing and prototyping of traditional recommender system methods, such as User KNN, Item KNN and FunkSVD Collaborative Filtering. It is developed and maintained by Gabriel Sepúlveda and Vicente Domínguez, advised by Prof. Denis Parra, all of them in Computer Science Department at PUC Chile, IA Lab and SocVis Lab.

Must-read Papers for Recommender Systems (RS)

The collection of papers about recommender system

[Python3.6] IEEE Paper "Matrix Factorization Techniques for Recommender Systems" by Koren,Bell,Volinsky

Implementation for Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews.

The implementation of "PARL: Let Strangers Speak Out What You Like", Libing Wu, Cong Quan, Chenliang Li, Donghong Ji, https://doi.org/10.1145/3269206.3271695

Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction

Movie Revenue & Ratings Prediction Using 5000 IMDB Movies [Python, Machine Learning, GitHub]

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

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.

Netflix data challenge hosted by PRML course in IITM, we secured 5th position as team Goodfellas

Movie Recommendation Using Matrix Factorization.

A chrome extension to predict star ratings according to the customer's review.

Elo Rating System written in Swift for Swift Package Manager

Recommender system with Netflix database using matrix factorization

Relations / rating prediction in trust-based social networks

Machine learning ------- rating prediction for the review of commenting restaurant

Designed a system that will use existing yelp data to provide insightful analysis and to assist existing business owners, future business owners to make important decisions about a new business or business expansion.

An Amazon product recommender system that predicts product ratings and review helpfulness based on linear regression and latent-fact model.

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.

Project With Partner; Using Data Analysis/ Visualization and ML to predict the rating an App would get on the Google Play Store

Browser extension for DMOJ to predict contest rating changes.

Opinion recommendation is a task, recently introduced, for consistently generating a text review and a rating score that a certain user would give to a certain product, which has never seen before. Input information driving recommendation is text reviews and ratings for this product contributed by other users and text reviews submitted by the user under consideration for other products. The aforementioned task faces the same problems emerging in text generation using neural networks, namely repetition and specificity. In this paper, it is experi- mentally demonstrated that by employing coverage loss during training, repetition is reduced without adding extra parameters. Furthermore, the amount of repetition in the generated text review is defined as a measure of the captured information. Such measure is used to improve rating score prediction significantly during testing.

Movies recommendation and rating prediction using collaborative filtering.

Predict ratings of google local reviews in order to better recommend the places to users based on historical data and the sentiment within.

for beginners tutorial

To predict if a customer will like a movie or not depending on his past ratings and preferences using RBMs.