2. Impetus
a) Shorten the distance between knowing what poets
you like and making great discoveries of new
poets to read
b) Map the vast interconnectedness, over time and
across the globe, of poets and their influences
c) Use the latest technology and techniques such as
crowd-sourcing and data science to better the
world of poetry overall
3. The Problem
a) People are not reading enough poetry
b) Good poetry has something to do with quality, but is also largely a matter of taste
c) Finding quality poetry that you enjoy, which inspires you to seek out more of the
same, requires a large investment of time and energy for the average person
d) This is partly because the average person does not have access to a trusted and
knowledgeable friend who can make customised recommendations
e) As a result, the average person is often left feeling they either don't like, or don't
understand, contemporary poetry (or both)
4. Goal
a) Move people from grey to white
b) Move people from white to blue
c) Increase engagement with poetry
5. Goal
a) Move people from grey to white
b) Move people from white to blue
c) Increase engagement with poetry
“I DON’T
GET
POETRY”
6. Similar Models
a) Pandora mapped the "music genome" and became
hugely successful as a music recommendation
service; Spotify in the UK soon followed suit
b) Wikipedia lets people both contribute and benefit from
expertise on a vast range of topics
c) Recommendations engines like that at Amazon are
proprietary and commercially-motivated
d) The main idea behind "crowd-sourcing" is that our
collective knowledge, shared, becomes a vast and
valuable repository--and this "hive mind" is smarter
than any one of us individually
7. Timeline
24 February 2016 Invitation-only Private Beta group begins testing
28 March 2016 Public Beta launches, seeded by 1,000 users contributing nearly
3,000 poets and over 4,000 votes
13 April 2016 Poet tips goes viral, gaining over 20,000 tips in less than five
days via poets sharing about it on social media
14. The Team
Robert Peake
Technology Executive
and poet
(London, UK)
Rebecca Roach
MFA candidate turned
entrepreneur, poet
(Indiana, USA)
Michael Dalvean
Professor, creator of
Poetry Assessor and
Jazz Musician
(Canberra, Australia)
Jennifer Moore
Professor and poet
(Ohio, USA)
19. Applications
a) Education: Teaching students to discover and reflect upon their own tastes
b) Library Sciences: Solving the "Catch-22" of the card catalogue (i.e. that you have to
know what you want before you go searching for it)
c) Digital Humanities: mapping the "poetry genome" by combining the latest
techniques in computational linguistics and network graph theory
d) Other forms of research an enquiry