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51 search results found

Tutorials ⭐ 847

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

Variational autoencoders for collaborative filtering

Dl Course ⭐ 201

Deep Learning with Catalyst

Recotour ⭐ 147

A tour through recommendation algorithms in python [IN PROGRESS]

Recommender_system_with_python ⭐ 124

recommender system tutorial with Python

Keras Movielens Cf ⭐ 75

A set of Jupyter notebooks demonstrating collaborative filtering using matrix factorization with Keras.

Second Springboard Capstone Project: A Movie Recommender

Spotify Song Recommendation Ml ⭐ 52

UC Berkeley team's submission for RecSys Challenge 2018

Movie Recommender Demo ⭐ 50

This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).

Kdd_cup_2020_debiasing_rush ⭐ 44

Solution to the Debiasing Track of KDD CUP 2020

Als_implementation ⭐ 43

Implementation of ALS algorithm from "Collaborative Filtering for Implicit Feedback Data"

🧮 Extended Latent Dirichlet Allocation for Collaborative Filtering in Recommender Systems.

Bellkoralgorithm ⭐ 33

A Python implementation of the Bellkor Algorithm

Variational Autoencoders Collaborative Filtering ⭐ 28

This repository contains python (using Keras) code implementing variational autoencoders for collaborative filtering on movielens and spotify data

Deep Autoencoder Recommendation ⭐ 26

Keras implementation of AutoRec and DeepRecommender from Nvidia.

Sequential Variational Autoencoders for Collaborative Filtering

Ranking-Critical Training for Collaborative Filtering

Recsys_autoencoders ⭐ 23

This project implements different Deep Autoencoder for Collaborative Filtering for Recommendation Systems in Keras

Movie_recommendation_engine ⭐ 23

This repository contains the code for building movie recommendation engine.

RACF (Recency Aware Collaborative Filtering) is the implementation of the "Recency Aware Collaborative Filtering for Next Basket Recommendation" original paper. which is a set of collaborative filtering approaches for solving the problem of predicting the Next Basket Recommendation.

Recsys19_hybridsvd ⭐ 21

Accompanying code for reproducing experiments from the HybridSVD paper. Preprint is available at https://arxiv.org/abs/1802.06398.

Ucl Drl Msc ⭐ 19

Learning to Recommend using a Deep Reinforcement Agent

Recommender Systems With Collaborative Filtering And Deep Learning Techniques ⭐ 19

Implemented User Based and Item based Recommendation System along with state of the art Deep Learning Techniques

Movie_recommendation_engine ⭐ 16

Movie Recommender based on the MovieLens Dataset (ml-100k) using item-item collaborative filtering.

SANSA - sparse EASE for millions of items

Quick Data Science Experiments 2017 ⭐ 14

Quick-Data-Science-Experiments

Recommender Systems For Implicit Feedback Datasets ⭐ 14

Matrix Factorization augmented with customer item meta data

Csce670_ncf ⭐ 14

Neural Collaborative Filtering for CSCE670

Collaborative_filtering ⭐ 12

Recommendation system for music.

Collaborative Filtering ⭐ 11

Recommendation System using Collaborative Filtering

Deeprecommender ⭐ 11

Training Deep AutoEncoders for Collaborative Filtering

Data Science Skills Practice ⭐ 11

Restaurant Recommendations With Yelp ⭐ 11

🍕Recommend new restaurants to Yelp users, using ratings predicted from reviews.

Grocery_recommendation ⭐ 10

Grocery Recommendation on Instacart Data

Neural_cf ⭐ 9

Comparing keras, pytorch and gluon using neural collaborative filtering

Collaborative Translational Metric Learning (ICDM 2018)

Case Study Ml Netflix Movie Recommendation System ⭐ 9

A Machine Learning Case Study for Recommendation System of movies based on collaborative filtering and content based filtering.

Recommendation Engine ⭐ 8

Recommendation engine and it's algorithms in python , R .

Cdn Recsys ⭐ 8

A hybrid recommender system for suggesting CDN (content delivery network) providers to various websites

Musicmagal ⭐ 7

Recommender System Demo ⭐ 7

Build and Deploy Recommender System on rorocloud

Twinkeydow ⭐ 7

it recommends github repos

Movie Recommender System ⭐ 6

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.

Hybrid Recommender System ⭐ 6

A repository for a machine learning project about creating a hybrid movie recommender system.

Msca 31005 Database Class Project ⭐ 6

The aim of our team was to use data from meetup.com to build a recommender system which will identify and suggest groups and activities to a member based on their interest and additional interests of similar members. Furthermore, a social network analysis was done to identify the relationship between groups and people.

Second-language acquisition modeling, that extends the task of knowledge tracing. PyTorch implementations of neural collaborative filtering, (TODO) deep knowledge tracking (LSTM and Memory Network) on Assistment09 benchmark dataset.

A Comparative Analysis Of Amazon Book Ratings Using Collaborative Filtering ⭐ 6

Juxtaposing different Recommender Algorithms by utilizing the concept of Collaborative Filtering to analyze the Amazon Book Ratings.

Recommendersystems ⭐ 5

Recommender Systems and Collaborative Filtering

Movie Recommendation Tutorial ⭐ 5

Mrf_neurips_2019 ⭐ 5

Markov Random Fields for Collaborartive Filtering

Thyfriendmyfriend ⭐ 5

Implementation of the NIPS'17 paper titled "Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation"

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