#TOA13 - Tech Opoen Air Recommender Hackathon

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#TOA13 - Tech Opoen Air Recommender Hackathon

  1. 1. The Recommender Challenge Hackathon plista GmbH 2013/08/02 Torben Brodt
  2. 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
  3. 3. Architecture
  4. 4. Tracking Success ● each time a recommender is chosen, plista will track its success.. for context and context combinations ???
  5. 5. Tracking Success ● "online evaluation" technology ● better than classical offline evaluation known from papers? ● cooperation with TU Berlin, aided by state ???
  6. 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. 7. The hackathon ● many interesting people ● get to know developers using ○ PHP, Java, NodeJS, Python ○ Redis, Storm, Elastic Search ○ Apache Mahout, Lucene ○ ...
  8. 8. The hackathon ● http://contest.plista.com/ ○ started 2 year ago ● New API in august ● News Recommender Challenge ○ ACM RecSys HongKong http://recsys.acm.org/
  9. 9. How to start (1/3) register at contest.plista.com
  10. 10. How to start (2/3) ● start implementation using examples ● http://contest.plista.com/wiki/example
  11. 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. 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. 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. 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
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