  Personalized search provides a potentially powerful tool, however, it is
limited due to the large number of roles that a person has: parent, employee,
consumer, etc. We present the role-relevance algorithm: a search technique that
favors search results relevant to the user's current role. The role-relevance
algorithm uses three factors to score documents: (1) the number of keywords
each document contains; (2) each document's geographic relevance to the user's
role (if applicable); and (3) each document's topical relevance to the user's
role (if applicable). Topical relevance is assessed using a novel extension to
Latent Dirichlet Allocation (LDA) that allows standard LDA to score document
relevance to user-defined topics. Overall results on a pre-labeled corpus show
an average improvement in search precision of approximately 20% compared to
keyword search alone.
