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Free keywords:
cs.SI,Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML,
Abstract:
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.
