Recommendation Systems Vipul Sharma

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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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Recommendation SystemsVipul Sharma

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1.5 million events80 million tickets sold

$1 billion in gross ticket salesEvents in 179 countries

Eventbrite by the Numbers

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Who am I?

Director of Data EngineeringStudied computer scienceMachine Learning, Analytics and Big DataSpam Detection, Consumer Data Mining, Infrastructure

linkedin.com/in/vipulsharma3@[email protected]

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Event Lifecycle

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Create an Event Order Now


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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

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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

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Recommendations – How?

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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

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Why Social Graph is not Enough

• Events are social• Events reflect your interests• Social graphs are dense• Interests shift while your graph doesn’t

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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


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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


• Mixed – Activity with Friends• Most powerful

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Implicit Social Graph

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Mixed Social Graph

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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

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Put it all together

Item TaxonomyUser InterestUser Graph/Interest GraphRankingRecommendations

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Final Product

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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

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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

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See it in action. Download our app:


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Thank You!@vipulsharma/ [email protected]