Name
Title | ||
|---|---|---|
Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction |
Abstract | ||
|---|---|---|
ABSTRACT In this paper we provide a description of the methods we used as team BanaNeverAlone for the ACM RecSys Challenge 2020, organized by Twitter. The challenge addresses the problem of user engagement prediction: the goal is to predict the probability of a user engagement (Like, Reply, Retweet or Retweet with comment), based on a series of past interactions on the Twitter platform. Our proposed solution relies on several features that we extracted from the original dataset, as well as on consolidated models, such as gradient boosting for decision trees and neural networks. The ensemble model, built using blending, and a multi-objective optimization allowed our team to rank in position 4. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3415959.3415998 | RECSYS |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolò Felicioni | 1 | 1 | 1.02 |
| Andrea Donati | 2 | 1 | 0.35 |
| Luca Conterio | 3 | 1 | 0.35 |
| Luca Bartoccioni | 4 | 1 | 0.35 |
| Davide Yi Xian Hu | 5 | 1 | 0.69 |
| Cesare Bernardis | 6 | 1 | 0.69 |
| Maurizio Ferrari Dacrema | 7 | 62 | 10.03 |