Integrated Expert Recommendation Model for Online Communities
This addresses the need for efficient expert identification in online communities, but it is incremental as it builds on existing methods like vector space models and PageRank.
The paper tackles the problem of finding expert users in online communities by proposing a cascaded model that combines content relevance and social influence, showing it effectively recommends experts who are both relevant to queries and influential in their areas.
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.