Graduate School and Research Center in Digital Sciences

Two stages approach for tweet engagement prediction

Dadoun, Amin; Harrando, Ismail; Lisena, Pasquale; Reboud, Alison; Troncy, Raphaël

Technical report, 24 August 2020

This paper describes the approach proposed by the D2KLab team for the 2020 RecSys Challenge on the task of predicting user engagement facing tweets. This approach relies on two distinct stages. First, relevant features are learned from the challenge dataset. These features are heterogeneous and are the results of different learning modules such as handcrafted features, knowledge graph embeddings, sentiment analysis features and BERT word embeddings. Second, these features are provided in input to an ensemble system based on XGBoost. This approach, only trained on a subset of the entire challenge dataset, ranked 22 in the final leaderboard.

Arxiv Bibtex

Title:Two stages approach for tweet engagement prediction
Keywords:Recommender System, Tweet Engagement Prediction, Knowledge Graph
Type:Report
Language:English
City:
Date:
Department:Data Science
Eurecom ref:6324
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Technical report, 24 August 2020 and is available at :
Bibtex: @techreport{EURECOM+6324, year = {2020}, title = {{T}wo stages approach for tweet engagement prediction}, author = {{D}adoun, {A}min and {H}arrando, {I}smail and {L}isena, {P}asquale and {R}eboud, {A}lison and {T}roncy, {R}apha{\"e}l}, number = {EURECOM+6324}, month = {08}, institution = {Eurecom}, url = {http://www.eurecom.fr/publication/6324},, }
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