Scott Triglia, MLconf 2013
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Scott Triglia, Search and Data Mining Engineer at Yelp
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Scott Triglia, MLconf 2013
- 2. Goals ● Show our thought process ● Expose some useful questions ● Practical solutions for new teams
- 3. Disclaimer This is a case study, not a prescription
- 4. Yelp 101
- 5. Our Topic Our Goal: Interesting businesses relevant to you right now
- 6. Before
- 7. Decision Time Brainstorming what matters with your ace team of devs
- 10. Don’t be shy Interesting reasons are half the point!
- 11. Know your Team The organizational context matters too: ● We have very little infrastructure to support large-scale ML ● Must scale to all Yelp data+users on day 1. ● Our team is small, and will be so for a while ● This is a first version of a (hopefully!) long lived product
- 12. So what do we build?
- 13. Guiding Principles 1) We know we need to solve a retrieval problem
- 14. Guiding Principles 2) Build for what you have, plan for expansion
- 15. Guiding Principles 3) We need to build a good product, not beat a benchmark
- 16. The Big Picture API Request Experts Experts Experts Elastic Search Final Results General flow: 1. Gather sufficient contextual information 2. Consult each expert for their top candidates 3. Wisely combine suggestions from each expert
- 17. Building the request API Request Experts Experts Experts Final Results ● From client: location, user_id ● Derived context ● Neighborhood preferences ● User preferences ● Time preferences Elastic Search
- 18. Expert Opinions API Request Experts Experts Experts Elastic Search Final Results Each expert handles a single reason and knows its own requirements. For example, a LikedByFriends expert would only return candidate businesses which one of the user’s friends had rated highly.
- 19. Expert Opinions API Request Experts Experts Experts Final Results Liked By Friends Expert: General Requirements: Open Now Sufficiently Nearby Expert Requirements: At least one friend gave it 5 stars Elastic Search
- 20. Expert Opinions API Request Experts Experts Experts Elastic Search Final Results Why an expert-based system? ● Think in terms of small, isolated components ● Implementation agnostic ● Adding, removing experts is trivial
- 21. Efficient Search API Request Experts Experts Experts Final Results What do we need from our datastore? ● Fast geographic filtering ● Simple but efficient sorting ● All of this happening in 100ms Elastic Search
- 22. Final Decisions API Request Experts Experts Experts Elastic Search Final Results How to combine expert results? We need to factor in: ● Must balance preferences (distance, rating, category) ● Should prefer better reasons when possible ● Sufficiently high quality candidates makes this very safe
- 23. Get to the point already!
- 24. Get to the point already!
- 25. Get to the point already!
- 26. Get to the point already!
- 27. Get to the point already!
- 28. Final Version
- 29. Extension Now that we’re iterating, what are our future plans? ● Richer context (user, location, etc.) ● Infrastructure support for faster ML prototyping ● Better personalized ranking ● Training data!
- 30. Summary So what are the takeaways for building a first recommender system? ● Solve your problem, not someone else’s ● Being cutting edge may not be the top priority ● Build for the tools you have, plan for what will come ● Good software engineering enables quality ML