Over the past few years, major web search engines have
introduced knowledge bases to offer popular facts about people, places,
and things on the entity pane next to regular search results. In
addition to information about the entity searched by the user, the
entity pane often provides a ranked list of related entities. To keep
users engaged, it is important to develop a recommendation model that
tailors the related entities to individual user interests. We propose a
probabilistic Three-way Entity Model (TEM) that provides personalized
recommendation of related entities using three data sources: knowledge
base, search click log, and entity pane log. Specifically, TEM is
capable of extracting hidden structures and capturing underlying
correlations among users, main entities, and related entities.
Moreover, the TEM model can also exploit the click signals derived from
the entity pane log. We further provide an inference technique to learn
the parameters in TEM, and propose a principled preference learning
method specifically designed for ranking related entities. Extensive
experiments with two real-world datasets show that TEM with our
probabilistic framework significantly outperforms a state of the art
baseline, confirming the effectiveness of TEM and our probabilistic
framework in related entity recommendation.