A significant portion of web search queries directly refers to named
entities. Search engines explore various ways to improve the user experience
for such queries. We suggest augmenting search results with {\em trivia facts}
about the searched entity. Trivia is widely played throughout the world, and
was shown to increase users' engagement and retention.
Most random facts are not suitable for the trivia section. There is skill
(and art) to curating good trivia. In this paper, we formalize a notion of
\emph{trivia-worthiness} and propose an algorithm that automatically mines
trivia facts from Wikipedia. We take advantage of Wikipedia's category
structure, and rank an entity's categories by their trivia-quality.
Our algorithm is capable of finding interesting facts, such as Obama's Grammy
or Elvis' stint as a tank gunner. In user studies, our algorithm captures the
intuitive notion of "good trivia" 45\% higher than prior work. Search-page
tests show a 22\% decrease in bounce rates and a 12\% increase in dwell time,
proving our facts hold users' attention.

本文提出了一种算法，根据维基百科的类别结构，自动挖掘搜索结果实体的值得收录的小知识，以增强搜索结果的用户体验。通过用户研究，证明该算法比之前的工作更能很好地抓到 “好的小知识”，能够让用户更好地停留在搜索页面上。