Users increasingly rely on social media feeds for consuming daily
information. The items in a feed, such as news, questions, songs, etc., usually
result from the complex interplay of a user's social contacts, her interests
and her actions on the platform. The relationship of the user's own behavior
and the received feed is often puzzling, and many users would like to have a
clear explanation on why certain items were shown to them. Transparency and
explainability are key concerns in the modern world of cognitive overload,
filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a
framework that systematically discovers, ranks, and explains relationships
between users' actions and items in their social media feeds. We model the
user's local neighborhood on the platform as an interaction graph, a form of
heterogeneous information network constructed solely from information that is
easily accessible to the concerned user. We posit that paths in this
interaction graph connecting the user and her feed items can act as pertinent
explanations for the user. These paths are scored with a learning-to-rank model
that captures relevance and surprisal. User studies on two social platforms
demonstrate the practical viability and user benefits of the FAIRY method.

本文介绍 FAIRY 框架，该框架可以系统地发现、排名和解释用户在社交媒体上的行为与他们收到的信息源之间的关系，通过将平台上的用户本地邻居建模为交互图，然后用学习排名模型对这些路径进行评分，从而为用户提供合适的解释来提高透明度和可解释性。

FAIRY: 一个用于理解用户行为与社交动态关系的框架

FAIRY: A Framework for Understanding Relationships between Users'  Actions and their Social Feeds

Learning from a real-world data stream and continuously updating the model
without explicit supervision is a new challenge for NLP applications with
machine learning components. In this work, we have developed an adaptive
learning system for text simplification, which improves the underlying
learning-to-rank model from usage data, i.e. how users have employed the system
for the task of simplification. Our experimental result shows that, over a
period of time, the performance of the embedded paraphrase ranking model
increases steadily improving from a score of 62.88% up to 75.70% based on the
NDCG@10 evaluation metrics. To our knowledge, this is the first study where an
NLP component is adaptively improved through usage.

本研究开发了一个自适应学习系统用于文本简化，从使用数据中改进基于学习排序模型的性能，并通过实验结果表明，该系统在 NDCG@10 评估指标上的得分从 62.88％提高到 75.70％。这是首个使用使用数据对 NLP 组件进行自适应改进的研究。