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

Tentative Timeline

Session #1 - #3:
  • Get to know everyone!
  • We will introduce the problem & why it matters.
  • Core Team shortlisted. Intensive work begins!
  • Core Team will work towards the core problem.
  • Will be recognized by AISC at the conclusion of the project!
    • [For your portfolio / blog / Social Media].
End of session #3 Apply to join the Core Team
  • Energy & competency are both important, but energy is a little more.
  • You can still keep up with the Working Group if you don’t make the Core Team, but only the Core Team will be spotlighted in any complied / published work.
Session #4+ [Tentative length: 1.5 months]
What after?
  • If at the end of the 2 month period, there are new areas we’d like to explore, then we may have a follow-up 2 month Working Group! And then another, and so on.

Solution Architecture + Resources

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Key Resources

Pre-Reqs (if you need):
Dealing with large datasets Recipe (opt): Click here
Pre-processing: Click here. The same for each paper. Skim to get some quick intuition.
Graph Recommender Systems: A Survey
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
Looking forward to meeting everyone in our study group! Please feel free to reach out if you have any questions about the planned project.
To jump in mid-way, just join our Slack channel and introduce yourself!
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