Fun Facts: Automatic Trivia Fact Extraction from Wikipedia
This work addresses the need for improved user engagement in search engines by providing curated trivia facts, though it is incremental as it builds on existing methods for fact extraction.
The paper tackles the problem of automatically extracting trivia facts from Wikipedia to enhance search engine results for named entities, achieving a 45% improvement in capturing 'good trivia' over prior work and increasing user engagement with a 22% decrease in bounce rates and 12% increase in dwell time.
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.