Twitter is currently one of the biggest social media platforms. Its users may
share, read, and engage with short posts called tweets. For the ACM Recommender
Systems Conference 2020, Twitter published a dataset around 70 GB in size for
the annual RecSys Challenge. In 2020, the RecSys Challenge invited
participating teams to create models that would predict engagement likelihoods
for given user-tweet combinations. The submitted models predicting like, reply,
retweet, and quote engagements were evaluated based on two metrics: area under
the precision-recall curve (PRAUC) and relative cross-entropy (RCE).
In this diploma thesis, we used the RecSys 2020 Challenge dataset and
evaluation procedure to investigate how well context alone may be used to
predict tweet engagement likelihood. In doing so, we employed the Spark engine
on TU Wien's Little Big Data Cluster to create scalable data preprocessing,
feature engineering, feature selection, and machine learning pipelines. We
manually created just under 200 additional features to describe tweet context.
The results indicate that features describing users' prior engagement history
and the popularity of hashtags and links in the tweet were the most
informative. We also found that factors such as the prediction algorithm,
training dataset size, training dataset sampling method, and feature selection
significantly affect the results. After comparing the best results of our
context-only prediction models with content-only models and with models
developed by the Challenge winners, we identified that the context-based models
underperformed in terms of the RCE score. This work thus concludes by situating
this discrepancy and proposing potential improvements to our implementation,
which is shared in a public git repository.

利用 RecSys 挑战数据集和评估程序，研究了仅利用上下文是否能够预测推文互动概率，并发现用户的先前互动历史、标签和链接的热度等特征对预测最具信息量。然而，相较于只基于内容的模型和挑战胜利者开发的模型，这些基于上下文的模型在 RCE 得分方面表现出较低。因此，提出了改进实现的潜在改进方法。