Sessions, Workshop datasets and notebooks while training with National University of Singapore powered by LICT and Ministry of ICT, Bangladesh.
Title | Domain | link |
---|---|---|
papers with code | Papers related to recommendation | link |
Google Tutorials | Tutorials | link |
Instacart market basket analysis | Market Basket Analysis | link github |
Starspace Facebook | Recommendation | link |
Food Discovery with Uber Eats: Recommending for the Marketplace | Recommendation | link |
Food Discovery with Uber Eats: Building a Query Understanding Engine | Recommendation | link |
Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations | Recommendation | link |
Implicit | Recommendation | link |
Explicit | Recommendation | link |
Meet Michelangelo: Uber’s Machine Learning Platform | Machine Learning Platform | link |
Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, and Xuanhui Wang. 2011. Click Shaping to Optimize Multiple Objectives. In KDD. ACM, New York, NY, USA, 132–140 | Recommendation | |
Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, and Xuanhui Wang. 2012. Personalized Click Shaping Through Lagrangian Duality for Online Recommendation. In SIGIR. ACM, New York, NY, USA, 485–494. | Recommendation | |
Meta-Graph: Few-Shot Link Prediction Using Meta-Learning | Meta learning | link |
Evaluation Metrics for Recommender Systems | Evaluation | link |
Popular evaluation metrics in recommender systems explained | Evaluation | link |
Recommender Systems — It’s Not All About the Accuracy | Evaluation | link |
Recommendation System Evaluation Metrics | Evaluation | link |
Getting Started with a Movie Recommendation System | Kaggle | link |
Film recommendation engine | Kaggle | link |
Movie Recommender Systems Using Neural Network | Kaggle | link |
Building_Recommender_System_with_Surprise | Github | link |
Collaborative Filtering with Neural Networks | Github | link |
Graph Convolutional Neural Networks for Web-Scale Recommender Systems | GCN Paper | link |
Forecasting at Uber: An Introduction | Forecasting | link |
Recommender Systems Handbook | Book | 1 2 |
ACM RecSys | Conferences | link |
Microsoft Research | Rec Github | link |
List of Recommender Systems | Github | link |
A Gentle Introduction to Recommender Systems with Implicit Feedback | Notebook | link |
Matrix Factorization Surprise Implementation | Github | link |
How Hulu Uses InfluxDB and Kafka to Scale to Over 1 Million Metrics a Second | Hulu Blog | link |
Collaborative Filtering for Implicit Feedback Datasets | Paper | link |
Datasets for recommendation systems | Datasets | link |
Building Recommendation Engines with PySpark | Recommendation / Pyspark | link |
Netflix Recommender System — A Big Data Case Study | Netflix recommendation | link |
Build a Recommendation Engine With Collaborative Filtering | Collaborative filtering | link |
Introducing TensorFlow Recommenders | Tensorflow Recommenders | link |
Building a Recommendation System in TensorFlow: Overview | Google Cloud | link |
Kernel Name | link |
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Food Recommendation Dataset Exploration | kernel |
Recommender System | kernel |
Recommendation engine exploration | kernel |
Recommendation engine | kernel |
Book Recommendations from Charles Darwin | kernel |
Market Basket Analysis for Marketing Analytics | kernel |