rating-prediction

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

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

[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.

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

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

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

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

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

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

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.

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.

Movie Recommendation Using Matrix Factorization.

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.