  Trivia is any fact about an entity, which is interesting due to any of the
following characteristics - unusualness, uniqueness, unexpectedness or
weirdness. Such interesting facts are provided in 'Did You Know?' section at
many places. Although trivia are facts of little importance to be known, but we
have presented their usage in user engagement purpose. Such fun facts generally
spark intrigue and draws user to engage more with the entity, thereby promoting
repeated engagement. The thesis has cited some case studies, which show the
significant impact of using trivia for increasing user engagement or for wide
publicity of the product/service.
  In this thesis, we propose a novel approach for mining entity trivia from
their Wikipedia pages. Given an entity, our system extracts relevant sentences
from its Wikipedia page and produces a list of sentences ranked based on their
interestingness as trivia. At the heart of our system lies an interestingness
ranker which learns the notion of interestingness, through a rich set of
domain-independent linguistic and entity based features. Our ranking model is
trained by leveraging existing user-generated trivia data available on the Web
instead of creating new labeled data for movie domain. For other domains like
sports, celebrities, countries etc. labeled data would have to be created as
described in thesis. We evaluated our system on movies domain and celebrity
domain, and observed that the system performs significantly better than the
defined baselines. A thorough qualitative analysis of the results revealed that
our engineered rich set of features indeed help in surfacing interesting trivia
in the top ranks.

Information Retrieval