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

scalable Recommeder System for e-commerece using LightFM package in python

Movie recommendation system in python that uses uses the lightfm recommender system library.

Creating a hybrid recommender system using LightFM. Learn how to tackle the cold start problem.

Pre-train Embedding in LightFM Recommender System Framework

This example uses the lightfm recommender system library to train a hybrid content-based + collaborative algorithm that uses the WARP loss function on the movielens dataset

with Kaggle datasets

Hybrid Recommender System using anime-recommendations-database and LightFm.

Recommender System using LightFM

Recommender system for generating music playlists based on LightFM model.

The code uses the lightfm recommender system library to train a hybrid content-based + collaborative algorithm that uses the WARP loss function on the movielens dataset. The movielens dataset contains movies and ratings from over 1700 users. Once trained, our script prints out recommended movies for whatever users from the dataset that we choose to terminal. ##Dependencies numpy (http://www.numpy.org/) scipy (https://www.scipy.org/) lightfm (https://github.com/lyst/lightfm) Install missing dependencies using pip ##Usage Once you have your dependencies installed via pip, run the script in terminal via python recommender.py

hybrid recommender system using lightfm

Quickly startup a lightFM recommender system

Movie Recommender system using LightFM library

Recommender System using LightFM library dataset

Recommender System Model - LightFM and ALS

No description available

This is the code for the Recommender System challenge for 'Learn Python for Data Science #3' by @Sirajology on YouTube. The code uses the lightfm recommender system library to train a hybrid content-based + collaborative algorithm that uses the WARP loss function on the movielens dataset. The movielens dataset contains movies and ratings from over 1700 users. Once trained, our script prints out recommended movies for whatever users from the dataset that we choose to terminal.

Using the movielens dataset. This project will tell the three movies liked by the specified user and will recommend three movies to the user

Implementation of recommender system using LightFM(hybrid) and LightGBM(content-based) Algorithms

Recommender System using LightFM framework to improve the accuracy of Deezer's music prediction engine.

This is a news recommender system that uses beautiful-soup to scrape news articles, their categories and descriptions to create a data dump. It then uses word embedding techniques such tf-idf, word2vec for content based news recommendation and LightRF, LightFM to explore hybrid and collaborative filtering based recommender models.

Recommender system using LightFM library

A complete hybrid recommender system using LightFM on MovieLens 100k. The project covers all stages from data preprocessing and training to evaluation and a local recommendation API. Ideal for top-N recommendation scenarios.

No description available

a simple recommender system using LightFM

Building a Recommender System with LightFM

No description available

No description available

Collaborative filter recommender system (Hadoop, Pyspark, lightfm)