Popular GitHub Repositories for Ratings

A reference-free metric for measuring summary quality, learned from human ratings.

Intention Mining in Social Networking. It Mines Emotions and polarity for the given keyword . For the keyword it searchers the twitter for the comments and analyzes the results for various events such as Election results, Sports prediction Movie ratings, Breaking news events such as demonetisation and many more. Bayes , Maximum Entropy and Hidden Markov models are used to analyse the results. It requires R shiny package installed.

In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.

FIFA Overall Player Ratings Prediction Using Regression Methods

Auto encoders based recommendation system

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 predic

Launched a distributed application using Spark and MLlib ALS recommendation engine to analyze a complex dataset of 10 million movie ratings from MovieLens.

This repository consists of analysis over Amazon fine food purchase reviews by customers. The data has been collected by Stanford Network Analysis Project(SNAP). This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.

KR3: Korean Restaurant Review with Ratings / Experiments on Parameter-efficient Tuning and Task-adaptive Pre-training

This study aims to investigate the effectiveness of three Transformers (BERT, RoBERTa, XLNet) in handling data sparsity and cold start problems in the recommender system. We present a Transformer-based hybrid recommender system that predicts missing ratings and ex- tracts semantic embeddings from user reviews to mitigate the issues.

Trope-Enabled Neural Network for Predicting Film Ratings

Testing optimizers and time-series algorithms, and assigning them Elo ratings.

Autobot is an intelligent chatbot that provides information about automotive complaints, recall campaigns, safety ratings and VIN details in the United States.

Anime Recommender adaptation of the BERTRec project with custom anime ratings dataset consisted of 54M ratings and 600000 users.

Beauty Ratings Predictor is a machine learning-powered application that predicts beauty ratings from images. Using computer vision and deep learning, it analyzes facial features to generate a rating based on trained datasets. The project is built with Gradio, making it easy to use and interact with.

Research project investigating the addition of confidence bounds to the drift diffusion model (DDM) in order to explain confidence ratings and their associated reaction times.

This AI Quiz Application is an interactive tool that generates and evaluates questions based on user-defined topics using Google Generative AI. It features a responsive design for seamless use on both desktop and mobile devices, provides real-time feedback on answers, and displays performance ratings to help users track their progress.

A machine learning-powered Book Recommendation System that suggests highly-rated books based on user-selected titles. It filters recommendations by ratings and displays book covers with additional details in an interactive Streamlit interface.

This Multi-Modal Movie Recommendation System leverages a combination of structured numerical features and deep text embeddings to provide accurate and personalized movie recommendations. Unlike traditional recommender systems that rely solely on user ratings or metadata, this model integrates numerical attributes (such as popularity and ratings)

Audio analysis, chart-ability ratings, with automated comparisons, charts and URLs display.