The document describes the plista Recommender Challenge Hackathon. It provides information on:
- plista's recommendation and advertising network which delivers over 8 billion impressions per month.
- The hackathon challenges participants to develop a recommender that implements plista's API to be evaluated on its success in tracking recommendations. The best recommender that is scalable and works for industry will win.
- Participants can use various programming languages and machine learning libraries. Starting involves registering, implementing examples from the wiki, and getting real-time recommendation data from plista to display on publishers.
- Recommender ideas suggested focusing on implicit feedback, incremental updates, and handling cross-domain recommendations within publisher slices of data.
2. What is plista
● recommendation
● advertising
● network
● many big publishers in DE, AT, CH, ..
● "other articles you might be interested.."
● >8 billion impressions, clicks, engages, .. pM
4. Tracking Success
● each time a recommender is chosen, plista
will track its success.. for context and
context combinations
???
5. Tracking Success
● "online evaluation" technology
● better than classical offline evaluation known
from papers?
● cooperation with TU Berlin, aided by state
???
6. The hackathon
● we open the data, you provide the
knowledge
● develop a recommender which implements
the http + json api
● plista will track the success, if you are smart,
be the winner for the the best recommender
● best is live, best is scalable and best will
work in industry
7. The hackathon
● many interesting people
● get to know developers using
○ PHP, Java, NodeJS, Python
○ Redis, Storm, Elastic Search
○ Apache Mahout, Lucene
○ ...
9. How to start (1/3)
register at
contest.plista.com
10. How to start (2/3)
● start implementation using examples
● http://contest.plista.com/wiki/example
11. How to start (2/3)
● start implementation using examples
● http://contest.plista.com/wiki/example
● have a github account?
● "fork" one of the example projects
● work on your local "clone"
● upload to your server
● enter url in your contest account
12. How to start (3/3)
● need a virtual server? ask us
● need old data? start replay from
webinterface
● try sending debug events from webinterface
● wait for team activation
● plista starts sending you real data
● your responses are displayed on real
publishers
13. Recommender ideas
● concentrate on implicit feedback
● think streaming / incremental
○ better to scale
○ faster results, new articles are better than old
articles?
● think about cross domain
○ contest is not allowed to mix items from different
domains/publishers
○ want knowledge of the full data, but candidate items
of a slice
14. How to go on?
improve the algorithms
● there will be a new api
● there will be more competition (SIGIR,
RecSys)
join the meetups: http://recommenders.de/
join the team: http://www.plista.com/career