Rakuten Viki Challenge Results

The Rakuten Viki Global TV Recommender Challenge - had come to an end on 31 August. We received a total of 567 submissions from 132 participants - one of the most highly anticipated and compelling challenge thus far! Immense effort were put in by the participates who wanted shine in the challenge. The ranking on the Public Leaderboard continually evolved, as participants kept improving their predictions and outdo each other.

 

We have created an leaderboard activity graph to show positions of participants from the challenge launch day. From the visualization, we can see the participants battling out to be first on the leaderboard

 

The accuracy of the participants' models were validated from an unseen segment of the Rakuten Viki dataset, which generated a score on the Private Leaderboard (the higher the score, the better).

Team Name Public Leaderboard Private Leaderboard
haipt 0.1733 0.1576
Team Merlion 0.2374 0.1560
GM 0.2267 0.1477
PpRedicts 0.2204 0.1312
gbenedek 0.2300 0.1309
lenguyenthedat 0.2149 0.1112
 

Rakuten Viki Challenge Final Presentation

 

Following all the hard work put in by the participants, we would like to invite everyone to our grand finale on September 16th where shortlisted participants will present their algorithms and findings to a panel of judges, and compete for the top prizes (S$8,000). Our guest of honor Kiren Kumar, Director, Infocomms & Media, Singapore Economic Development Board, will be giving a welcome message. It is an opportunity not to be miss for the DEXTRA community as for the first time in DEXTRA history, the presentation will be open to public.

 

All the finalists are prominent data scientists such as PhDs and researchers from leading MNCs, SMEs, Start-ups and Research Institutes. Here are a brief preview of some of the highlights that will be shared by each team during the Final Presentations:

 
  • Team haipt – a co-founder and CTO of Teevers – used recommender to build user's preference vector. The most popular videos were used as recommendeded content for infrequent users.
  • Team Merlion of 7 A*Star data scientists, adopted a 3-step approach: classify > rank > filter, to give personalised recommendations. They dived deeply into the data and revealed a lot of insights.
  • Team GM - a research scientist at A*Star Data Analytics Department – combined the popularity and classification methods to make recommendations for infrequent and frequent users respectively.
  • Team PpRedicts - a data analyst at Facebook. He crafted a custom solution to combine metadata from movies, viewing behaviour and user preferences to generate predictions.
  • Team gbenedek - an Associate Professor of Corvinus University of Budapest and a founder of an analytics company - visualised the connections among videos using graph theory, which allowed identification of popular and similar videos for recommendation.
  • Team lenguyenthedat - a Senior Data Technologist at Commercialize TV - solution blended several models into a framework with a vision of allowing Rakuten-Viki to generate recommendations suited to different use cases.
 

Please click on the Register Now button below and reserve a seat for the event now!

 
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RECENT POSTS

  • Rakuten-Viki Final Presentation Event

    The Rakuten-Viki Global TV Recommender Challenge has finally come to a successful closure on the 16 September 2015. Six Teams (Team Merlion, Team GM, Team Haipt, Team Pritish,Team Gbenedek & Team Lenguyenthedat) were invited to present publicly in front of a pool of audiences and the judges.

  • Rakuten Viki Challenge Results

    Finalist teams are announced! We would like you to join us for the final presentation event where shortlisted teams will present their algorithms and insights to you.

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