Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

Keras implementation of AutoRec and DeepRecommender from Nvidia.

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

This repository consists a set of Jupyter Notebooks with a different Deep Learning methods applied. Each notebook gives walkthrough from scratch to the end results visualization hierarchically. The Deep Learning methods include Multiperceptron layers, CNN, GAN, Autoencoders, Sequential and Non-Sequential deep learning models. The fields applied includes Image Classification, Time Series Prediction, Recommendation Systems , Anomaly Detection and Data Analysis.

In this notebook, we have analysed the data from movielens dataset and tried to implement recommender system for movies. For this dataset, our main challenge was sparsity of user-movie matrix which means that the number of movie which are rated by user are very less compared to number of movies which are not rated by that same user. As part of data pre-processing, various type of matrix formation has been implemented as per the requirement of all algorithms used in this notebook. we have tried to analyse both neural network and tradition algorithms for recommender system in this notebook. In deep learning, we have implemented Restricted boltzmann machine, Deep Autoencoder, and simple deep neural network to predict the ratings and provide the recommendation based of predicted ratings for the user which has not rated that movie before in the dataset. In traditional algorithms, we have implemented various method of collaborative filtering such as SVD Matrix factorization, SVD++ and Hybrid model. Our analysis is centric towards the results achieved from deep learning algorithms and traditional algorithms by comparing the MSE cost function for all.

This Repositary delves deep into the building of Recommendation Systems using Self-Supervised Pattern Recognition Algorithm using Stacked Autoencoders and also using Advanced Self-Supervised Undirected Energy Based Modelling Algorithms like Restricted Boltzmann Machines

Building a deep learning Movie Recommendation System using a Stacked Autoencoder

A Deep Learning-based Tip Recommendation System for personalized user tips using Autoencoder with K-means clustering and the Deep Embedding Clustering (DEC) algorithm. Harnessing the power of Yelp datasets, this project optimizes tip generation through innovative retrieval-augmented generation (RAG) techniques.

This project explores deep learning-based collaborative filtering using autoencoders to build a recommendation system. The model learns latent representations of users and items to predict personalized recommendations.

Internet Movie Recommender Database is a website made using HTML, CSS, and Django which uses deep learning to recommend movies to users according to their preferences. An autoencoder trained on the ml-25m dataset using Pytorch was used for making the actual recommendation.

We propose a way of deep collaborative filtering with Adversarial Autoencoders(AAES) for tag recommendation with a multinomial likelihood function. Our probability model is able to exceed the limited modeling capabilities of linear models and allows us to explore the complex co-occurrence relationships between tags and items on large-scale tag recommendation datasets. In addition, label smoothing is introduced to alleviate overfitting, and adjusted to tag recommendation scenario by modify the construction of the true probability.

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Python_AutoEncoder_Deep_Learning_Recommendation_System

DeepMovieRec is a deep learning-based movie recommendation system that leverages stacked autoencoders to learn user preferences from historical ratings. It predicts unseen movie ratings with high accuracy, offering personalized and intelligent recommendations.

Deep Autoencoders for movie recommendations using Pytorch

Developing deep learning-based movie recommendation systems using Autoencoders for personalized user suggestions.

Movie Recommendation Web App that utilizes Deep AutoEncoders Networks

Autoencoder-based Recommendation System: Integrating Spark and Deep Learning models

“Deep learning–based player recommendation system using Autoencoders for feature compression and Cosine Similarity for high-quality similarity matching.”

Developed a deep learning-based movie recommendation system using autoencoder neural networks to generate personalized suggestions based on user preferences.

Movie recommendation system using Autoencoders in PyTorch. Built as part of my learning journey in deep learning and collaborative filtering.

Deep learning course 3 projects: Classification and Regression with a NN in Pytorch, Transfer Learning and Recommendation system using Autoencoder

Utilized PyTorch's deep learning capabilities to construct a Movie Recommendation System incorporating stacked autoencoders. Employed optimization methodologies to enhance the efficiency and precision of personalized movie recommendations.

This project implements a hybrid movie recommendation system using machine learning (K-Means clustering, PIP similarity) and deep learning (autoencoders) on the MovieLens dataset to provide personalized and diverse recommendations.

End-to-end AI-powered Netflix & OTT analytics and recommendation system using Machine Learning, Deep Learning (Autoencoders), Semantic Search, and Streamlit interactive dashboard.

An intelligent Streamlit-based web app for automated and ML-enhanced data cleaning, evaluation, and recommendation. Supports traditional methods, ML-based strategies, and deep learning (Autoencoders), with learning-based recommendations and PDF summaries.

This project highlights the application of deep learning in the development of recommendation systems and showcases the capabilities of Stacked Autoencoders in understanding and predicting user preferences