A Recommender System for Predicting User Engagement in Twitter

The RecSys Challenge is a traditional competition among Recommender Systems’ (RS) researchers. The 2014 edition is focused on predicting the amount of interaction achieved by tweets related to movies. In this paper, we present an approach to participate in the 2014 RecSys Challenge. Our approach consists of three steps: (i) using binary classification methods in order to split the tweets into two lists, those having user engagement equal to zero, and those having user engagement different from zero; (ii) each list is sorted through the use of regression methods; and (iii) is performed a concatenation of the two lists and a sort of tweets. To validate our approach we tested 126 configurations and verify that the settings using MovieTweetings dataset, Naïve Bayes classifier and Linear Regression, obtained the best results: nDCG@10 = 0.9037242.

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