Design and compare the performances of Information Retrieval Models of TF-IDF, Cosine Similarity, BM25. Implemented query expansion using psuedo relevance feedback to display better results.
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Nov 6, 2017 - HTML
Design and compare the performances of Information Retrieval Models of TF-IDF, Cosine Similarity, BM25. Implemented query expansion using psuedo relevance feedback to display better results.
Built a system to suggest anime to the users-based user viewing history and similarity of content on data from myanimelist.net, a popular source for anime content.
A web application which recommends movies to the user.
using feature maximisation for summarizing scientifc documents
Analysis of a movie dataset containing 1000 movies and then we built a Recommendation system based on the dataset
Recommendation of similar images to the given image using ResNet50, K-Means and cosine similarity.
Recommending movies using k-means clustering and cosine similarity
Exploring Machine Learning in production by deploying a Document-Similarity application on the cloud using Flask, Docker and Heroku
Determining similarity between two sentences in terms of semantic using pre trained Universal Sentence Encoder from TensorFlow.js
📝 Exploratory analysis on Linkin Park lyrics
Recommend movies to users using Sentiment Analysis on Text data
Popularity based Recommendation System, Content Based Recommendation System, Cosine Similarity
A natural language processing and machine learning project that predicts spam messages and explains how it does so
This repository contains a recommender system based on K-Mean Clustering combine with Content Based Filtering on Junior High School in Bandung, Indonesia dataset.
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.
The sample code repository leverages Azure Text Analytics to extract key phrases from the product description as additional product features. And perform text relationship analysis with TF-IDF vectorization and Cosine Similarity for product recommendation.
EDA, Pre-processing, 6 Recommendation Systems Techniques: * Popularity-Based, * Cosine Similarity Collaborative Filtering, * Matrix Factorization Collaborative Filtering, * Clustering, * Content-Based Filtering, * Hybrid Recommendation System.
An assignment on preprocessing of text including tokenization, stop word removal, noise reduction, and stemming
Search engine for the Lex Fridman Podcast 🎤
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