Learning Best Practices on Recommender Systems
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Updated
Oct 17, 2019 - Jupyter Notebook
Learning Best Practices on Recommender Systems
🍪 example for recon-engine
A graph-based citation network for paper recommender engine
The project develops an application that suggests to the reader more similar articles to that he already read. It uses the embedding algorithms of headlines to create their own numerical representation, which allows to compute the similarity between headlines and get the most similar ones.
Recommender engine using SageMaker KMeans clustering for a CloudGuru challenge.
The App aims at helping people to fight from depression and communicate with them at a 1-1 basis alongside giving them suggestions based on their choices which will be taken from the user in various formats such as MCQ for a one time basis and Diary entries of user on a daily basis. The app aims to help mainly people who are introvert and don't …
Content Based Movie Recommender
Tell-and-Show is a project for open recommendations that uses the AGPLv3 license to protect *data* and to consider said data as the source for machine learning models.
Cross modal, cross cultural, cross lingual, cross domain, and cross site global OER network
Incremental recommender engine built with Golang & Redis.
This repository provides some recommender engine models.
Tiktok is an advanced multimedia recommender system that fuses the generative modality-aware collaborative self-augmentation and contrastive cross-modality dependency encoding to achieve superior performance compared to existing state-of-the-art multi-model recommenders.
web app in Python using Flask framework
Exploring Machine Learning methods to provide the best recommendation for Expedia
一个Go语言开发的开源推荐系统, Gorse open source recommender system engine
Link Prediction Recommendation System with Node2Vec
The objective of this project is to build a kNN-based recommender system in order to predict the top 5 movie based on a given movie, in this case "The Post". As there is no need for classification or regression, the nearestNeighbors model and neighrbors() method are used to find the 5 most closely related films.
Pick Me A Flick: A content filtering based Movie Recommendation Engine .
This repository contains recent research papers, datasets, and source codes (if any) for Group Recommendation
recommender systems algorithms
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