There are 1 repository under cosine-similarity-scores topic.
A fuzzy matching string distance library for Scala and Java that includes Levenshtein distance, Jaro distance, Jaro-Winkler distance, Dice coefficient, N-Gram similarity, Cosine similarity, Jaccard similarity, Longest common subsequence, Hamming distance, and more..
Implementation of TextRank with the option of using pre-trained Word2Vec embeddings as the similarity metric
A web-app which can be used to get recommendations for a series/movie, the app recommends a list of media according to list of entered choices of movies/series in your preferred language using Python and Flask for backend and HTML, CSS and JavaScript for frontend.
A PyPI package that does extractive text summarizer using Cosine Methods in NLTK.
Mencari Kecocokan Antara Aku dan Dia Menggunakan Metode Cosine Similarity
Recipe Genie is a recipe recommendation system that recommends recipes to users based on the ingredients they have at home.
Movie recommendation system based on popularity and also using KNN and Cosine similarity. 🎥🍿
Web search engine to retrieve most relevant web-pages for user search query from web-pages crawled on the UIC domain
Collaborative filtering based book recommendation model deployed using flask
This is the Repository for the Mini Project done using the flask and python libraries, required as a part of the course curriculum.
This repository contains a simple code to compare two sentences based on their semantic similarity scores using a Universal sentence encoder.
The Programm tries to determine the cosine similarity scores for a set of words in question. Cosine similarity scores indicate the contextual similarity between words.
This repository hosts a number of short data science solution with code snippets ready to be used in various data science applications
A simple python repository for developing perceptron based text mining involving dataset linguistics preprocessing for text classification and extracting similar text for a given query.
Repository aimed at building a simple recommender system for a content based dataset. The textual information is analysed so as to utilised as a more concrete piece of information!
Demos to test modelling and classification algorithms for face recognition
In this project I am using the tf - idf algorithm and cosine similarity to find the similarity of two strings.
Portfolio Project.ipynb and Recommendation.py are the finalized Jupiter notebook scripts for this project. Other files are a work in progress to migrate into a web app.
This is a book recommendation system based on the book rating data from GoodReads_100k dataset. The dataset contains 100k book.
A Simple conversational chatbot built using NLU concepts. The project uses reddit comments taken from 2015, which has about 1.7 billiion interactions.
Implementation of Cosine Simlarity algorithm without using any Machine Learning library
An application to store a collection of questions and answers data found on Stackoverflow in an index using Elastic and perform a text search on the stored Q&A's based on semantic meaning.
This project uses machine learning to create a personalized bookrecommendation system. By combining collaborative filtering and content-based filtering, it analyzes user preferences and book attributes to suggest tailored book recommendations. The system offers real-time updates and accurate predictions to enhance the user experience.
Implemented various spellcheck techniques like cosine similarity, jaccard similarity and levenshtein distance. Open to any further contributions.
Machine learning solution to recommend 10 out of 8000 agents to new clients with 33% success rate
Assignments completed for CP423: Text Retrieval and Search Engines. Collaborated with Abigail Lee and Myisha Chaudhry
Build an algorithm/model that can quantify the degree of similarity between the two text-based on Semantic similarity. Semantic Textual Similarity (STS) assesses the degree to which two sentences are semantically equivalent to each other.
This is a speaker verification system uses Total Variability and Projection Matrix. Intersession variability was compensated by using backend procedures, such as linear discriminant analysis (LDA) and within-class covariance normalization (WCCN), followed by a scoring, the cosine similarity score. In literature this approach named i-vectors.
Q&A chatbot powered by a Retrieval-Augmented Generation (RAG) approach to provide answers to neural network-related questions.
Image Search Engine