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Predicting User Engagement in Twitter with Collaborative Ranking 
Ernesto Diaz-Aviles∗, Hoang Thanh Lam, Fabio Pinelli, St...
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Predicting User Engagement in Twitter with Collaborative Ranking. IBM Research - Ireland @ RecSys Challenge 2014 (3rd Place Winners)

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Problem:
- Current methods of Collaborative Filtering (CF) evaluation: (i) quality of a predicted rating or (ii) the ranking performance for top-n recommended items

- These evaluation methods are rather limiting and neglect other dimensions that could better characterize a well-perceived recommendation

- The task in this work: predict which items generate the highest user engagement

Contribution:

*** Collaborative ranking approach for user engagement prediction in Twitter ***

Published in: Science
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Predicting User Engagement in Twitter with Collaborative Ranking. IBM Research - Ireland @ RecSys Challenge 2014 (3rd Place Winners)

  1. 1. Predicting User Engagement in Twitter with Collaborative Ranking Ernesto Diaz-Aviles∗, Hoang Thanh Lam, Fabio Pinelli, Stefano Braghin, Yiannis Gkoufas, Michele Berlingerio, and Francesco Calabrese IBM Research – Ireland Problem ▶ Current methods of Collaborative Filtering (CF) evaluation: (i) quality of a predicted rating or (ii) the ranking performance for top-n recommended items ▶ These evaluation methods are rather limiting and neglect other dimensions that could better characterize a well-perceived recommendation ▶ The task in this work: predict which items generate the highest user engagement Contribution: Collaborative ranking approach for user engagement prediction in Twitter Twitter IMDb      (1) Extract features (2) Learn ranking function for user engagement prediction    Feature Extraction ▶ User rating, F1 = ruid ▶ Deviation of user rating from the median of previous user ratings, i.e., F2 = ruid − ˜ru ▶ Average user engagement from her history, i.e., F3 = engagement(u)0.5 ▶ F4 =

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