Algolia introduction - DEMO and Ranking Formula

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

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