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。

基于上下文的推文互动预测

Context-Based Tweet Engagement Prediction

In this work, we introduce the notion of Context-Based Prediction Models. A
Context-Based Prediction Model determines the probability of a user's action
(such as a click or a conversion) solely by relying on user and contextual
features, without considering any specific features of the item itself. We have
identified numerous valuable applications for this modeling approach, including
training an auxiliary context-based model to estimate click probability and
incorporating its prediction as a feature in CTR prediction models. Our
experiments indicate that this enhancement brings significant improvements in
offline and online business metrics while having minimal impact on the cost of
serving. Overall, our work offers a simple and scalable, yet powerful approach
for enhancing the performance of large-scale commercial recommender systems,
with broad implications for the field of personalized recommendations.

引入了 Context-Based Prediction Models 这一概念，通过仅依赖用户和环境特征，而不考虑物品特定特征的方式来预测用户的行为概率，该方法在许多有价值的应用中表现出显著优势，包括训练辅助的上下文模型以估计点击概率，并将其预测作为 CTR 预测模型的一个特征进行加入，实验证明这种改进在离线和在线业务指标上带来了显著的提升，而对服务成本几乎没有影响，总体上，我们的工作为大规模商业推荐系统的性能提升提供了一种简单且可扩展却强大的方法，对个性化推荐领域具有广泛的影响。