Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Learning document aboutness from implicit user feedback and document structure
Proceedings of the 18th ACM conference on Information and knowledge management
Resolving surface forms to Wikipedia topics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A large-scale sentiment analysis for Yahoo! answers
Proceedings of the fifth ACM international conference on Web search and data mining
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In many cases, when browsing the Web, users are searching for specific information. Sometimes, though, users are also looking for something interesting, surprising, or entertaining. Serendipitous search puts interestingness on par with relevance. We investigate how interesting are the results one can obtain via serendipitous search, and what makes them so, by comparing entity networks extracted from two prominent social media sites, Wikipedia and Yahoo! Answers.