A collaborative filtering based recommendation engine and NPM module built on top of Node.js and Redis. The engine uses the Jaccard coefficient to determine the similarity between users and k-nearest-neighbors to create recommendations. This module is useful for anyone with a database of users, a database of products/movies/items and the desire to give their users the ability to like/dislike and receive recommendations.

Content based and collaborative filtering based recommendation and personalization engine implementation on Hadoop and Storm

A javascript library for collaborative filtering and recommendation engines designed for node.js

Node.js Recommendation Engine based on Collaborative Filtering

Build a recommendation engine using Django & a Machine Learning technique called Collaborative Filtering.

Recommendation engine based on collaborative filtering

A Movie Recommendation Engine that uses collaborative and content based filtering to suggest movies.

Recommendation engine using collaborative filtering and matrix factorization

creating a recommendation engine for anime based on model based collaborative filtering

A Hybrid recommendation engine built on deep learning architecture, which has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor

Building a Hybrid Recommendation Engine: Content-based Filtering + Collaborative Filtering (Spark, PySpark, Apache Drill)

This is a book recommendation engine built using a hybrid model of Collaborative filtering, Content Based Filtering and Popularity Matrix.

Python script demonstrating spark streaming and Kafka implementation using an e-commerce website like product recommendation engine based on item-based collaborative filtering. 🐍. 💥

Building a very simple recommendation engine using collaborative filtering

Item based collaborative filter recommendation engine

Survey of collaborative filtering techniques and their effectiveness as a movie recommendation system

A recommendation engine for an e-commerce website using collaborative filtering

User Based Movie Recommendation System based on Collaborative Filtering Using Netflix Movie Dataset

Hybrid recommendation engine combining collaborative filtering, content-based matching, and context-aware analysis along with LLM insights

The recommendation engine provides personalized product recommendations to users based on their preferences and historical interactions with products. The engine uses collaborative filtering and content-based filtering techniques to make relevant and accurate product recommendations.

Automated Resume Screening System (With Dataset) A web app to help employers by analysing resumes and CVs, surfacing candidates that best match the position and filtering out those who don't. Description Used recommendation engine techniques such as Collaborative , Content-Based filtering for fuzzy matching job description with multiple resumes. Prerequisites Software textract==1.6.3 requests==2.22.0 Flask==1.1.1 gensim==3.8.0 sklearn==0.0 PyPDF2==1.26.0 autocorrect==0.4.4 nltk==3.4.5 contractions==0.0.21 textsearch==0.0.17 inflect==2.1.0 numpy==1.17.2 pdfminer.six==20181108 Python 3.6.0 |Anaconda 4.3.0 (64-bit)| Dataset Link1 : https://s3.ap-south-1.amazonaws.com/codebyte-bucket/Resume%26Job_Descriptions.zip Mirror : https://drive.google.com/open?id=17M9oDPip5JFFFNJhDCBQKy8BMqoyxajU Running localhost Run pip install -r requirements.txt to install dependencies Simply run this command from root directory. Login Username :admin Password : whitetigers2018 python app.py Running using Docker Create the docker image running: docker build -t arss . Run the container: docker run -it -p 5000:5000 arss

This is a collaborative filtering-based recommendation engine for a music streaming service

Used Alternating Least Square method to build a recommender system in Spark [PySpark, Databricks, Python, Machine Learning]

Posts/Feeds recommendation engine based on content based and collaborative filtering methods

I made a Movie recommendation Engine using Content Based Filtering, Collaborative filtering. In content Based Filtering I done Preprocessing of data, used Average-weighted-rating technique, Statistical Approach, Vectorization, Cosine Similarity. While in Collaborative I done some Preprocessing of data, Make Pivot table, Vectorization, Correlation Scores, SVD and KNN Algorithms.

A Collaborative Filtering based music recommendation engine.

An Recommendation Engine based on the Collaborative filtering algorithm as taught by Andrew Ng.

:computer: An Amazon Office Products Recommendation Engine using Item-Item collaborative filtering and Matrix Factorization

Beer recommendation engine based on collaborative filtering algorithm

Redis module that implements a Jaccardian similarity and memory-based collaborative filtering recommendation engine