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Artificial Intelligence for IT/ITES by ISS National University of Singapore

Sessions, Workshop datasets and notebooks while training with National University of Singapore powered by LICT and Ministry of ICT, Bangladesh.

Resources:

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

Kaggle Notebooks:

Kernel Name link
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

Secondary Resources

  1. what-is-product-recommendation
  2. product-recommendations
  3. ecommerce-ai-recommender-system
  4. product-recommendations
  5. syte

Nvidia

  1. Recommender Systems, Not Just Recommender Models

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Sessions workshop slides, datasets, and notebooks while training with the National University of Singapore for IT/ITES industry in the field of Artificial Intelligence program.

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