论文标题
推文参与预测的两个阶段方法
Two Stages Approach for Tweet Engagement Prediction
论文作者
论文摘要
本文介绍了D2KLAB团队针对2020年Recsys挑战提出的方法，即预测推文面向的用户参与的任务。这种方法取决于两个不同的阶段。首先，从挑战数据集中学到了相关功能。这些功能是异质的，是不同学习模块的结果，例如手工制作的功能，知识图嵌入，情感分析功能和bert单词嵌入。其次，这些功能是在基于XGBoost的集合系统输入中提供的。这种方法仅在整个挑战数据集的一个子集上进行培训，在最终排行榜中排名22。
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.
