There are 1 repository under rating-prediction topic.
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
Leetcode Rating Predictor built with Node. Browser extension and web interface.
recommender system library for the CLR (.NET)
Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems
Must-read Papers for Recommender Systems (RS)
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.
The collection of papers about recommender system
Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction
[Python3.6] IEEE Paper "Matrix Factorization Techniques for Recommender Systems" by Koren,Bell,Volinsky
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
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.
Netflix data challenge hosted by PRML course in IITM, we secured 5th position as team Goodfellas
Movie Recommendation Using Matrix Factorization.
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.
Elo Rating System written in Swift for Swift Package Manager
Predict the rating that a user will give to a book given their past book ratings.
A chrome extension to predict star ratings according to the customer's review.
Project With Partner; Using Data Analysis/ Visualization and ML to predict the rating an App would get on the Google Play Store
Movies recommendation and rating prediction using collaborative filtering.
Recommender system with Netflix database using matrix factorization
Relations / rating prediction in trust-based social networks
Predict ratings of google local reviews in order to better recommend the places to users based on historical data and the sentiment within.
Movie Rating Prediction based on NETFLIX dataset using Low Rank Matrix factorization technique.
Machine learning ------- rating prediction for the review of commenting restaurant
Using the MovieLens dataset with Surprise to compare different algorithms for rating prediction, and also create a movie recommendation system on top of it.
电子科技大学 2020 级《信息检索》课程代码。
The Glicko-2 rating system for Scala and Scala.js
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.
Browser extension for DMOJ to predict contest rating changes.
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.
for beginners tutorial