"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

May 25, 2021

Lecture Notes - Google Machine Learning System Design Mock Interview

 

Key Notes

  • Billions of videos, Millions of users
  • Recommend video for right user / time / context
  • Start small, Scale to current needs
  • Recommendation based on the entire corpus of content
  • Meat in the middle approach
  • Pick a Small dataset
  • Video, User1, Completion %%
  • Video, User2, Completion %%
  •  Use for a similar taste
  • Similarity-based on some model
  • The user doesn't like video
  • Collaborative filtering techniques
  • Activity, Attributes, Demographics
  • User - User
  • Demographics, Age, locale, Historical data
  • Weightage of recent vs previous topics
  • Topic Continuity based recommendations
  • Reference / Approximation
  • Comparing across videos/users
  • Auto recommendations / Next list to display
  • Map user/item vector and compare with Cartesian steps
  • Edge cases - Cold start / new user content
  • The initial filter of videos based on preferences then include trending videos in the domain, Add scoring
  • Use choices long/short videos 
  • Ranking step / Filtering Step

My thoughts

  • Clustering and they recommending based on cluster they belong
  • Custom models for each locale, interests then getting it prioritized
Machine Learning System Design (YouTube Recommendation System)

Key Notes

Multitask ranking system


  • Two stage pattern
  • Select narrowed down candidates
  • Ranking approach to check those candidates
  • Funnel Fashion
  • Could be SQL query / Watched by users
  • Regression to narrow down further
  • Finding relevant candidates for users


  • Current watched video + context
  • 700 users / second / scalable system





  • Predict the probability of engagement for a video
  • For each value multiple predictions
  • Combine all outputs, ensemble them



Keep Thinking!!!

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