GitHub topics: rating-prediction

Repositories

maxruther/Movie-List-Madness

A long-term series of movie-related personal projects, centered on database-building and web-scraping.

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caserec/CaseRecommender

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

Language: Python - Size: 1.35 MB - Last synced at: 5 days ago - Pushed at: over 1 year ago - Stars: 496 - Forks: 92

cheungdaven/DeepRec

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

Language: Python - Size: 14.6 MB - Last synced at: 15 days ago - Pushed at: almost 3 years ago - Stars: 1,151 - Forks: 293

zenogantner/MyMediaLite

recommender system library for the CLR (.NET)

Language: C# - Size: 29.2 MB - Last synced at: 29 days ago - Pushed at: about 5 years ago - Stars: 506 - Forks: 190

ninadmehta95/Yelp-Analysis-and-Rating-Prediction

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.

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SysSn13/leetcode-rating-predictor

Leetcode Rating Predictor built with Node. Browser extension and web interface.

Language: JavaScript - Size: 4.89 MB - Last synced at: 6 days ago - Pushed at: about 1 year ago - Stars: 528 - Forks: 53

loserChen/Awesome-Recommender-System

The collection of papers about recommender system

Size: 83 KB - Last synced at: 10 days ago - Pushed at: about 1 year ago - Stars: 87 - Forks: 19

gasevi/pyreclab

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.

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VinAIResearch/RecGPT

RecGPT: Generative Pre-training for Text-based Recommendation (ACL 2024)

Language: Python - Size: 47.9 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 20 - Forks: 0

bhaskrr/restaurant-reviews-5-class-rating-prediction-

This repo contains the dataset and notebook for the kaggle restaurant reviews five class rating prediction

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bhaskrr/amazon-books-4-class-rating-prediction

This repository holds the dataset and notebooks for the Amazon Books dataset 4 class Rating prediction

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11a55an/employee-rating-prediction

Employee Rating Prediction using Random Forest and XGBoost in R

Language: R - Size: 1.01 MB - Last synced at: 10 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

Danishyousuf19/Decision-Tree-and-Random-Forest

This contains all the project in which i have used Decision Trees and Random Forest to Predict output

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Iliaromanov/Google-Play-App-Rating-Prediction

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

Language: Jupyter Notebook - Size: 6.38 MB - Last synced at: 10 months ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 1

codewithpandey/predicto

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

Language: Python - Size: 303 KB - Last synced at: 22 days ago - Pushed at: over 3 years ago - Stars: 3 - Forks: 3

shreyansh-2003/CinemAI.Insights-Movie_Script_Analyser

CinemAI is a one stop solution solution created for movie buffs. The fullstack app has a movie scene-wise and character-wise analysis feature. It also has genre classification, movie, movie rating prediction and age restriction prediction based on a movie's script.

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Junnie13/wildrift-vs-mobilelegends

Analysis of Google PlayStore reviews for “League of Legends: Wild Rift” and “Mobile Legends: Bang Bang”

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WilliamOdinson/YelpRecommendation

This project develops a Yelp-based restaurant rating prediction algorithm using NLP to analyze review texts and generate a weighted unified rating. It then employs methods like SVD, ALS, SGD, and Random Forest to recommend restaurants based on these ratings.

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MichalRedm/DM-project2

This repository contains the code for the second project for Data Mining classes at the Poznań University of Technology, created by the team Kung Fu Pandas.

Language: Python - Size: 1.77 MB - Last synced at: 2 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

harshraj11584/Paper-Implementation-Matrix-Factorization-Recommender-Systems-Netflix

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

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mrcaidev/information-retrieval 📦

电子科技大学 2020 级《信息检索》课程代码。

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bhaskar6f1/Latent-Factor-Model-for-rating-prediction

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

Language: Python - Size: 1.95 KB - Last synced at: about 1 year ago - Pushed at: over 7 years ago - Stars: 8 - Forks: 1

KyleOng/starreco

State-of-The-Art Rating-based RECOmmendation system: pytorch lightning implementation

Language: Python - Size: 217 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 4 - Forks: 0

hegongshan/Recommender-Systems-Paper

Must-read Papers for Recommender Systems (RS)

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ilovemyminutes/CF-NADE

CF-NADE PyTorch Implementation

Language: Python - Size: 16.6 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

vinayak1998/Amazon-Rating-Prediction

Predicting Amazon ratings based on reviews by Text Classification using the Naive Bayes Algorithm.

Language: Python - Size: 20.9 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 0 - Forks: 0

gionanide/OpiRec

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.

Language: Python - Size: 5.74 MB - Last synced at: over 1 year ago - Pushed at: about 2 years ago - Stars: 6 - Forks: 0

rafaelhferreira/cta_rating_prediction

Repo for the paper "Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features" presented at SIGIR 2023

Language: Python - Size: 45.9 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

niuchenglei/ssm-dnn

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

Language: Python - Size: 19.5 MB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 10 - Forks: 4

micts/RecommenderSystem

Movie Recommendation Using Matrix Factorization.

Language: Python - Size: 58.6 KB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 6 - Forks: 0

akshayreddykotha/rating-prediction-google-local

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

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WHUIR/PARL

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

Language: Python - Size: 22.5 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 21 - Forks: 5

mrdimosthenis/glicko2

The Glicko-2 rating system for Scala and Scala.js

Language: Scala - Size: 12.7 KB - Last synced at: 2 months ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

jacobceles/Movie-Recommendation-Rating-Prediction

Using the MovieLens dataset with Surprise to compare different algorithms for rating prediction, and also create a movie recommendation system on top of it.

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Vishwesh4/Netflix-prize

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

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lovesaroha/Movies-Recommendation-System

Movies recommendation and rating prediction using collaborative filtering.

Language: JavaScript - Size: 610 KB - Last synced at: 2 months ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 0

guoyang9/A3NCF

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

Language: Python - Size: 13.7 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 23 - Forks: 6

chaitanyakasaraneni/recommendationsystem

Predict the rating that a user will give to a book given their past book ratings.

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C-Ritam98/Rating_Prediction

Movie Rating Prediction based on NETFLIX dataset using Low Rank Matrix factorization technique.

Language: Python - Size: 126 KB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

vinhqdang/trust_predict_ciao_epinions

Relations / rating prediction in trust-based social networks

Language: R - Size: 59.6 MB - Last synced at: over 1 year ago - Pushed at: about 8 years ago - Stars: 2 - Forks: 4

Ninjaclasher/dmoj-rating-predictor

Browser extension for DMOJ to predict contest rating changes.

Language: JavaScript - Size: 112 KB - Last synced at: 2 months ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 1

someaditya/yelp-rating-prediction

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.

Language: Python - Size: 76.2 KB - Last synced at: about 2 years ago - Pushed at: about 7 years ago - Stars: 4 - Forks: 2

VatsalSoni301/Restaurant_Rating_Prediction

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.

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