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6Tribes was a new aged social media app, that put user interests at the heart of connectivity; rather than who you know, it’s about what you know and what you want to know.
Lead Data Scientist + User Researcher | 6Tribes | 2016 | Acquired to become DriveTribe
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6Tribes was positioned to be provide intelligent recommendations for interest groups based based on the users likes/dislikes. Initially we began with no data
to handle this inference, so began leaning on the giant of Social Media, being Facebook.
FB began limiting access to data and increasing permissions. however we were able to get access to photos, locations, current groups and recent posts.
The smallest amount of data can be powerful when combining it with additional sources. With photos you can extract geo data, which when used alongside event and landmark data can help you understand what types of food people like and where they hang.
From posts and shared links, you can extract keywords for hobbies and political beliefs. The what groups or people someone chooses to follow they provide a very strong direct indicator of what a persons interest lie.
NLP techniques for longer content included bag of words, n-grams and TF-IDF. For shorter content we used the Rapid Automatic Keyword Extraction along with cleaning processes.
In order to process and analyse the data we used Natural Language techniques, along with API services incl. Prismatic, Foursquare and IBM.
Across apps that present recommendations, users are often intrigued as to why an item has been recommended, even furious if a recommendation is incorrect. With Social DNA, we wanted to present users back their transformed data in a meaningful way hence did this via profile tags of their inferred interests. Users could then additionally delete and add tags, which allowed a feedback loop for algorithmic improvements.
Most apps make it easy to share assets and invite users via a array of platforms. However, with the use of Facebook, we were able to make contact invitation more intelligent. Through using Facebook we can see the connected friends whom has liked a post. If this is a repeated occurrence, this creates a weak signal they may also have this as a common interest. Hence the 6Tribes user, upon wanting to share can have more intelligent invitee recommendations. The invitee then becomes more willing to join, as the content itself appears relevant to them.
From collated interests we recommended Tribes. These were formed based on interest weighting, alongside business and marketing objectives to appease to our growing user base. We matched using ElasticSearch matching and creating custom scoring based on both extracted interests. Tribes also contain keywords evolved in a similar process we’ve discussed above, allowing us to reinforce and evolve based on member postings.
Strong Python Development especially in API development and Data Science libraries such as Numpy, PIL and Flask.
Experience and love of creative coding techniques including Arduino's, Raspberry Pi's and Processing.
Ability to prototype ideas through both design and code, using Figma, Sketch, HTML, JS and CSS.
