Understanding Twitter Engagement with a Click-Through Rate-based Method
Andrea Fiandro, Jeanpierre Francois, Isabeau Oliveri, Simone Leonardi,, Matteo A. Senese, Giorgio Crepaldi, Alberto Benincasa, Giuseppe Rizzo
TL;DR
This paper introduces the POLINKS solution for the RecSys Challenge 2020, focusing on click-through rate analysis to improve Twitter engagement prediction, and compares its effectiveness with gradient boosting models.
Contribution
The paper presents a click-through rate-based approach for Twitter engagement prediction and evaluates its performance against gradient boosting models in a competitive setting.
Findings
POLINKS ranked 6th in the challenge
Click-through rate analysis improves engagement prediction
Comparison shows competitive performance of the proposed method
Abstract
This paper presents the POLINKS solution to the RecSys Challenge 2020 that ranked 6th in the final leaderboard. We analyze the performance of our solution that utilizes the click-through rate value to address the challenge task, we compare it with a gradient boosting model, and we report the quality indicators utilized for computing the final leaderboard.
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Taxonomy
TopicsRecommender Systems and Techniques · Multimedia Communication and Technology · Caching and Content Delivery