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.
Text of Eventbrite sxsw
1. Recommendation Systems Vipul Sharma
2. Eventbrite by the Numbers1.5 million events 80 million tickets sold $1 billion in gross ticket salesEvents in 179 countries 3. Who am I?Director of Data EngineeringStudied computer scienceMachine Learning, Analytics and Big DataSpam Detection, Consumer DataMining, Infrastructurelinkedin.com/in/vipulsharma3@email@example.com 4. Post EventCreation Event LifecycleOrganizationDiscoverySale 5. Create an Event Order Nowmarketplace 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 suggestionsFacebook 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 GraphGraph Your friends like Your friendsCollaborativeLady Gaga sowho shareFiltering Item-you will like your interestItem SimilarityLady Gaga in music, techYou like Godfather (Facebook, Linkeand moviesCollaborative so you will like dIn)Filtering User-Scarface (Netflix) are attendingUser SimilarityPeople who boughtSXSWa 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 metadata 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 doesnt 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 13. Implicit Social Graph 14. Mixed Social Graph 15. Who is similar to me?...Who is more similar to me? A two-step process: Identify clusters (via social graph); use theinterest 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 dataBuilding a Social Graph does the clustering for you Users do most of the work They self-select into accurate clustersModeling 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 togetherItem TaxonomyUser InterestUser Graph/Interest GraphRankingRecommendations 17. Final Product 18. Future Content DiscoverySearch 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 andrecommendations generated by friends The social graph will improve searchReviews Lack personalization Trust on Internet < Trust of friends 19. Future Content DiscoveryEntry Point More recommendation-based funnels More interconnected funnels Friends suggestions, similar items, editorialpicks, popular among similar users, etcRecommendation Systems More relevant, with more user data Finer graphs 20. Questions? See it in action. Download our app:eventbrite.com/eventbriteapp 21. Thank You!@vipulsharma/ firstname.lastname@example.org