Recommender Systems and Search Engines

Recommender Systems and Search Engines

Starts: June 29 - 12 noon ET
In this group, we discuss the basic algorithms and fundamentals of different ML techniques used to personalize services online.
Prior experience using Python is required for this project. We recommend that user’s are also familiar with scikit-learn, Tensorflow/PyTorch, Pandas and Numpy for ML-related modelling tasks. Linear Algebra knowledge is essential as well

Working Group Leads / Advisors

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Omar Nada, PhD. | Lead Applied Scientist II @ Microsoft
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Suhas Pai | Advisor CTO @ Bedrock AI
❗ Meeting Link:
 ❗ Meeting Time: 12-1 PM ET on Wednesdays (starting the 29th of June)
 ❗ Slack channel [Communication point]: click here to join

Project Overview

  • Practice organizing and structuring big datasets for streamlined use in ML projects
  • Examine model performance and robustness when tested against future transactions
  • Learn how different algorithms from different fields like GNN, DL and more can be leveraged in personalization models
  • Stretch Goal: Quantify the impacts of different models on the dataset utilizing different parts of the input data and building a personalization model that aids in showing users the products that they are really interested in. Determining the offline metrics appropriate for such a project ranging from precision, recall, CTR, accuracy and more. Write up a short article that examines this problem and share your findings

Tentative Project Timeline

Major Milestones
Expected time to finish
Get familiar with the project domain
2-3 weeks
Download & process Yelp Review data
1 week
Test different features and determine the right attributes
1 week
Develop two ML classifiers
4 weeks
Building offline metrics appropriate
1 week
Summarize the difference between the models identifying potential weakness and strengths
1 week

Solution Architecture + Resources

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All Resources / Recipes before have either been coded out or expert checked for quality
Pre-prcessing: The same for each paper. Skim to get some quick intuition.
  • Convolutional Networks: TBA
  • Graph Structures: TBA
  • Collaborative Filtering: TBA
Neural Graph Collaborative Filtering (NGCF):
Self Supervised Graph Learning for Recommendations (SGL):
Model comparison component:
Precision & Recall (more emphasis on Recall):
We will write an article on what we learn as we compare the models above. The Core Team will be recognized as co-auhors.

Why join?

Aggregate Intellect hosts one of the most diverse ML communities in the world. Over the course of the working group
  • You’ll get an immersion into that community & walk out with some cool new friends.
  • Learn how to download and interact with large scale real data in Python
  • Advance your ML skills by working on real world problems with classification algorithms of increasing sophistication
  • Contribute to a study area (land cover classification) which has major impacts to resource management practices, wildlife habitat protection and in advancing our understanding of the Earth’s biophysical systems
Looking forward to meeting everyone in our study group! Please feel free to reach out if you have any questions about the planned project.