Eventbrite sxsw
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Eventbrite talk at SXSW interactive 2013. The talk is about recommendation systems. The talk goes in details of what, why, how and future of recommendation systems.
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Eventbrite sxsw
- 1. Recommendation Systems Vipul Sharma
- 2. Eventbrite by the Numbers 1.5 million events 80 million tickets sold $1 billion in gross ticket sales Events in 179 countries
- 3. Who am I? Director of Data Engineering Studied computer science Machine Learning, Analytics and Big Data Spam Detection, Consumer Data Mining, Infrastructure linkedin.com/in/vipulsharma3 @vipulsharma vipul@eventbrite.com
- 4. Post Event Creation Event Lifecycle Organization Discovery Sale
- 5. Create an Event Order Now marketplace
- 6. Recommendation - What? • Mechanism to match users with their needs • Ecommerce – what users should buy. • Content – what users should browse. • Amazon – Product suggestions • Netflix – Movie suggestions •Facebook – Newsfeed • LinkedIn – People you many know • Eventbrite – Event Picks for you
- 7. Recommendations - Why? • User Acquisition • Bring users to your service • Build long-term trust • Happy customers are happy advertisers • User Engagement • Engage users with strategic placements • Build site navigation with various funnels • Expose more inventory to users • Conversion • Upsell • Convert less popular inventory • Example Attendee Newsletter
- 8. Recommendations – How? Interest Social Graph Graph Your friends like Your friends Collaborative Lady Gaga so who share Filtering – Item- you will like your interest Item Similarity Lady Gaga in music, tech You like Godfather (Facebook, Linke and movies Collaborative so you will like dIn) Filtering – User- Scarface (Netflix) are attending User Similarity People who bought SXSW a camera also (Eventbrite) bought batteries Item Hierarchy (Amazon) You bought a camera so you need batteries (Amazon)
- 9. Reason of Progression? • User data vs Item data • It was hard to collect user meta data vs item meta data • Items < Users • Items are less dynamic than users • Technology Changes • Social graphs • Big Data • Cloud • Crowd Sourcing
- 10. Why Social Graph is not Enough • Events are social • Events reflect your interests • Social graphs are dense • Interests shift while your graph doesn’t
- 11. Determining User Interests • Ask Users • Keep it frictionless • Explain the benefits • Learn from User Activity • What they bought, browsed, etc • Maintain a consistent taxonomy • Ask publisher • Use mathematical models • Use crowd sourcing • Use Facebook • Make sure your taxonomy maps with FB intrest data
- 12. Social Graphs • Implicit Graph - Activity • Connections based on activity • Interests trump relationships • We all create an interest graph • Explicit Graph - Friends • Friends who do not share your interests • Implicit graph is more active than explicit • Explicit graph does not change with your interests • Mixed – Activity with Friends • Most powerful
- 15. Who is similar to me?... Who is more similar to me? • A two-step process: Identify clusters (via social graph); use the interest graph to rank recommendations within that cluster • Is a user more similar to one person in his graph or another? • Preferences of the most similar connection will be ranked highest • Clustering applies detailed data from a single user to a group of users who are similar • This eliminates the need to ask each user in that group for detailed data •Building a Social Graph does the clustering for you • Users do most of the work • They self-select into accurate clusters •Modeling is another option • Models require that you collect learning data from users– but this creates friction • Who is more similar to me? •Recommendation is a Ranking Problem
- 16. Put it all together Item Taxonomy User Interest User Graph/Interest Graph Ranking Recommendations
- 17. Final Product
- 18. Future – Content Discovery Search • Excellent ability to match user queries with content • Limited understanding of each individual user • Limited understanding of user graph • People place the most trust in content and recommendations generated by friends • The social graph will improve search Reviews • Lack personalization • Trust on Internet < Trust of friends
- 19. Future – Content Discovery Entry Point • More recommendation-based funnels • More interconnected funnels • Friends’ suggestions, similar items, editorial picks, popular among similar users, etc Recommendation Systems • More relevant, with more user data • Finer graphs
- 20. Questions? See it in action. Download our app: eventbrite.com/eventbriteapp