  Online communities have become vital places for Web 2.0 users to share
knowledge and experiences. Recently, finding expertise user in community has
become an important research issue. This paper proposes a novel cascaded model
for expert recommendation using aggregated knowledge extracted from enormous
contents and social network features. Vector space model is used to compute the
relevance of published content with respect to a specific query while PageRank
algorithm is applied to rank candidate experts. The experimental results show
that the proposed model is an effective recommendation which can guarantee that
the most candidate experts are both highly relevant to the specific queries and
highly influential in corresponding areas.
