Introduction The problem we were trying to solve: - 6tribes: interest based social networking - Matching of user to users - Matching user to user groupings

Making recommendations is easy. - People that did X also did Y - We didn’t have any people!

Making good recommendations is hard. - User expectations are high! - Getting lost in data exploration - Ensuring fresh recommendations - Scaling recommendation engine

Making recommendations without data is impossible. Or is it? Engineering - Integrating and delivering continuously with CircleCI and OpBeat - Using data from Facebook and iPhone music and photo library - Enriching our data using external API’s like: HereAPI, FourSquare, Prismatic, Alchemy, iTunes - Using Flask to design a Rest API: Integration of data science and scala backend - increase development turn around time - Using Elasticsearch to deliver recommendations at scale - Using AWS for our data pipeline

User feedback - Internal testing of recommendations - being careful not to overfit (Anthony’s complaints and use of music) - Ensuring we act on feedback from our test users

Issues we had - Tech debt and lifecycle - Facebook signin: matured and out-dated data: not really representative of the user: show example and restrictions of data - No users! - Cost of getting data - API usage, specific data sets needed -> limited funding

Key Takeaways - How to enrich data - Building fast - Getting feedback - Ensuring scalability - Understanding of complexities of recommendation systems