Algolia introduction - DEMO and Ranking Formula

Algolia introduction - DEMO and Ranking Formula

Presentation material at Global Engineers Meetup: Search Engineering by Mericari on June 20th, 2019.

Cc8a208e174943d1da814783841abd50?s=128

Eiji Shinohara

June 20, 2019
Tweet

Transcript

  1. Algolia introduction DEMO and Ranking Formula Eiji Shinohara Senior Manager,

    Solutions Engineer - Japan eiji@algolia.com @shinodogg
  2. Who am I? • Eiji Shinohara ◦ Twitter: @shinodogg ◦

    Blog: https://shinodogg.com ◦ 1st Japan-based employee at
  3. Who am I? • Eiji Shinohara ◦ Worked at Rakuten

    ▪ Learned English - Eigo Chotto Dekiru ▪ My Search engineering SENSEIs: Minoru san & Kazu san (now at Mercari) ◦ Worked at Amazon Web Services ▪ In charge of Japanese Startups ▪ Used to visit Mercari office (1st floor is MIZUHO Bank) years ago
  4. Our Investors $74M Funding Founded 2012 Employees 350+ Our Business

    Algolia in a nutshell 100 Countries Customers 7,000+ Search queries/mo 60B+
  5. 17 Regions 70+ Data centers Offices Infrastructure Regions 60B+ Searches/mo

    150B+ API calls/mo Global Availability across 17 regions worldwide
  6. Our 7,000+ customers across 100 countries trust Us Retail Media

    Other industries
  7. In Japan

  8. https://www.globenewswire.com/news-release/2019/05/22/1840823/0/en/Algolia-Announces-Global-Exp ansion-Into-Japan.html

  9. DEMO

  10. Ranking Formula

  11. TF-IDF Term Frequency

  12. TF-IDF Term Frequency

  13. TF-IDF Term Frequency Inv. Doc. Frequency

  14. TF-IDF Term Frequency Inv. Doc. Frequency 0.92 0.44

  15. Useful when We don’t know the type of data in

    advance Content is completely different from one document to the other No popularity taken into account TF-IDF Term Frequency Inv. Doc. Frequency
  16. Relevance in Algolia Structured Data Data that already exists in

    your Database You know this data (what is useful in it for search, what is not)
  17. Title or description? First or last word? Exact? How many

    follower/likes?
  18. Ranking Formula criteria Not based on a TF-IDF variation Uses

    8 rules to evaluate/measure relevance
  19. Ranking Formula criteria 1. Typo 2. Geo (if applicable) 3.

    Words (if applicable) 4. Filters 5. Proximity 6. Attribute 7. Exact 8. Custom
  20. Tie-breaking algorithm

  21. Ranking formula The Tie Breaking Algorithm Lego bricks example 3

    attributes or qualities : • Shape • Color • Studs
  22. Ranking formula The Tie Breaking Algorithm • Rank the set

    by applying a series of criteria • Eliminate ties, criterion per criterion • The order we apply criterion has a big impact on the final ranking of the whole set Goal
  23. Tie-breaking Algorithm • Color (Blue > Red > White) Ranking

    Formula A • Studs (less is best) • Shape ( > )
  24. Tie-breaking Algorithm Color Shape Studs (Blue > Red > White)

    Start Rect > Round Less is best
  25. Tie-breaking Algorithm • Shape ( > ) Ranking Formula B

    • Color (Blue > Red > White) • Studs (less is best)
  26. Tie-breaking Algorithm Color (Blue > Red > White) Shape Rect

    > Round Studs Less is best Start
  27. Tie-breaking Algorithm • Color (Blue > Red > White) •

    Shape ( > ) Ranking Formula A • Studs (less is best) • Shape ( > ) • Color (Blue > Red > White) Ranking Formula B • Studs (less is best)
  28. ANY QUESTIONS? Thank you. Eiji Shinohara Senior Manager, Solutions Engineer

    - Japan eiji@algolia.com @shinodogg