All Projects → sukeshsangam → Deep-Learning-Model-for-Hybrid-Recommendation-Engine

sukeshsangam / Deep-Learning-Model-for-Hybrid-Recommendation-Engine

Licence: other
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

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Deep-Learning-Model-for-Hybrid-Recommendation-Engine

Cornac
A Comparative Framework for Multimodal Recommender Systems
Stars: ✭ 308 (+1521.05%)
Mutual labels:  recommendation-system, recommendation-engine
Recommenders
Best Practices on Recommendation Systems
Stars: ✭ 11,818 (+62100%)
Mutual labels:  recommendation-system, recommendation-engine
Recdb Postgresql
RecDB is a recommendation engine built entirely inside PostgreSQL
Stars: ✭ 297 (+1463.16%)
Mutual labels:  recommendation-system, recommendation-engine
recommender
NReco Recommender is a .NET port of Apache Mahout CF java engine (standalone, non-Hadoop version)
Stars: ✭ 35 (+84.21%)
Mutual labels:  recommendation-system, recommendation-engine
raptor
A lightweight product recommendation system (Item Based Collaborative Filtering) developed in Haskell.
Stars: ✭ 34 (+78.95%)
Mutual labels:  recommendation-system, recommendation-engine
Chameleon recsys
Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems
Stars: ✭ 202 (+963.16%)
Mutual labels:  recommendation-system, recommendation-engine
Movie Recommender System
Basic Movie Recommendation Web Application using user-item collaborative filtering.
Stars: ✭ 85 (+347.37%)
Mutual labels:  recommendation-system, recommendation-engine
retailbox
🛍️RetailBox - eCommerce Recommender System using Machine Learning
Stars: ✭ 32 (+68.42%)
Mutual labels:  recommendation-system, recommendation-engine
laracombee
📊 A Recombee integration for Laravel
Stars: ✭ 91 (+378.95%)
Mutual labels:  recommendation-system, recommendation-engine
MachineLearning
Machine learning for beginner(Data Science enthusiast)
Stars: ✭ 104 (+447.37%)
Mutual labels:  recommendation-system, recommendation-engine
Machine-Learning
Examples of all Machine Learning Algorithm in Apache Spark
Stars: ✭ 15 (-21.05%)
Mutual labels:  recommendation-system, recommendation-engine
recommender system with Python
recommender system tutorial with Python
Stars: ✭ 106 (+457.89%)
Mutual labels:  recommendation-system, recommendation-engine
mildnet
Visual Similarity research at Fynd. Contains code to reproduce 2 of our research papers.
Stars: ✭ 76 (+300%)
Mutual labels:  recommendation-system
toptal-recommengine
Prototype recommendation engine built to accompany an article on Toptal Blog
Stars: ✭ 109 (+473.68%)
Mutual labels:  recommendation-system
Deeplearning Image Similarity
Deep learning based image similarity search for product recommendations
Stars: ✭ 94 (+394.74%)
Mutual labels:  recommendation-engine
BERT4Rec-VAE-Pytorch
Pytorch implementation of BERT4Rec and Netflix VAE.
Stars: ✭ 212 (+1015.79%)
Mutual labels:  recommendation-system
Knowledge Graph based Intent Network
Learning Intents behind Interactions with Knowledge Graph for Recommendation, WWW2021
Stars: ✭ 116 (+510.53%)
Mutual labels:  recommendation-system
TIFUKNN
kNN-based next-basket recommendation
Stars: ✭ 38 (+100%)
Mutual labels:  recommendation-system
Recommendation-System-Baseline
Some common recommendation system baseline, with description and link.
Stars: ✭ 34 (+78.95%)
Mutual labels:  recommendation-system
awesome-graph-self-supervised-learning-based-recommendation
A curated list of awesome graph & self-supervised-learning-based recommendation.
Stars: ✭ 37 (+94.74%)
Mutual labels:  recommendation-system

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

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].