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RecSys Challenge 2014 
User Engagement as Evaluation 
Alan Said, Simon Dooms, Babak Loni, Domonkos Tikk 
@alansaid, @sidooms, @babak_loni, @domonkostikk 
@recsyschallenge
Today’s Program 
Welcome 
Analysis of the Challenge 
Presentations (4) 
Coffee Break 
Presentations (2) 
Poster Session 
Discussion & Closing Remarks 
ACM RecSys Challenge 2014 2 
9:00-9:15 
9:15-9:30 
9:30-10:30 
10:30-11:00 
11:00-11:30 
11:30-12:15 
12:15-12:30
First, About Datasets 
• In the beginning there was MovieLens 
Good for analysis, experimentation, simulation, and seed 
data 
• Now: old, static, synthetic 
Less interesting for current day experiments 
• New dataset to fill the void: MovieTweetings 
Daily updated dataset of movie ratings extracted from 
IMDb ratings posted to Twitter 
Recent movies, growing, natural dataset 
ACM RecSys Challenge 2014 3
The Challenge 
• Why stop at ratings? 
• Tweets have much more information! 
• Focus on ‘user interaction’ 
• How can we predict user engagement? 
How does that benefit recsys? 
ACM RecSys Challenge 2014 4
MovieTweetings Extended 
• MovieTweetings dataset + Twitter metadata 
• +200,000 tweets (1 year, 24 days) 
ACM RecSys Challenge 2014 5
Some Numbers 
• 225 teams registered, from 50 countries 
6 
ACM RecSys Challenge 2014
Some Numbers 
• The dataset was downloaded 407 times 
7 
ACM RecSys Challenge 2014 
More than the number of teams!
Some Numbers 
• 236 leaderboard updates by 36 teams 
• 18 papers submitted 
• 13 papers accepted 
• 7 presentations 
• 7 posters 
ACM RecSys Challenge 2014 8
RecSys Challenge 2014 
Presentations
Leaderboard, The Results 
ACM RecSys Challenge 2014 10 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12
RecSys Challenge 2014 
Poster Session
Discussion 
• Are some ratings more important than others? 
• Which external data source was useful? 
• Which are the important features of the tweets? 
• What else can we do with this dataset? 
ACM RecSys Challenge 2014 12

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RecSys Challenge 2014 Workshop Introduction

  • 1. RecSys Challenge 2014 User Engagement as Evaluation Alan Said, Simon Dooms, Babak Loni, Domonkos Tikk @alansaid, @sidooms, @babak_loni, @domonkostikk @recsyschallenge
  • 2. Today’s Program Welcome Analysis of the Challenge Presentations (4) Coffee Break Presentations (2) Poster Session Discussion & Closing Remarks ACM RecSys Challenge 2014 2 9:00-9:15 9:15-9:30 9:30-10:30 10:30-11:00 11:00-11:30 11:30-12:15 12:15-12:30
  • 3. First, About Datasets • In the beginning there was MovieLens Good for analysis, experimentation, simulation, and seed data • Now: old, static, synthetic Less interesting for current day experiments • New dataset to fill the void: MovieTweetings Daily updated dataset of movie ratings extracted from IMDb ratings posted to Twitter Recent movies, growing, natural dataset ACM RecSys Challenge 2014 3
  • 4. The Challenge • Why stop at ratings? • Tweets have much more information! • Focus on ‘user interaction’ • How can we predict user engagement? How does that benefit recsys? ACM RecSys Challenge 2014 4
  • 5. MovieTweetings Extended • MovieTweetings dataset + Twitter metadata • +200,000 tweets (1 year, 24 days) ACM RecSys Challenge 2014 5
  • 6. Some Numbers • 225 teams registered, from 50 countries 6 ACM RecSys Challenge 2014
  • 7. Some Numbers • The dataset was downloaded 407 times 7 ACM RecSys Challenge 2014 More than the number of teams!
  • 8. Some Numbers • 236 leaderboard updates by 36 teams • 18 papers submitted • 13 papers accepted • 7 presentations • 7 posters ACM RecSys Challenge 2014 8
  • 9. RecSys Challenge 2014 Presentations
  • 10. Leaderboard, The Results ACM RecSys Challenge 2014 10 1 2 3 4 5 6 7 8 9 10 11 12
  • 11. RecSys Challenge 2014 Poster Session
  • 12. Discussion • Are some ratings more important than others? • Which external data source was useful? • Which are the important features of the tweets? • What else can we do with this dataset? ACM RecSys Challenge 2014 12