Abstract
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.
Highlights
Knowledge sharing has been a research topic for the last decade
Mean Average Precision (MAP): This metric is the mean of the average precision scores for each query
This research proposes a model for expert recommendation in online community (EXREC)
Summary
Knowledge sharing has been a research topic for the last decade. Knowledge sharing environment includes repositories (containing those socially constructed as with Wikipedia [15]) as well as online forums designed for sharing knowledge and expertise. Discussion groups and forums, an emerging type of web-based communities for users to share knowledge and experiences or to provide social support, have attracted many users in various fields. Each user can share experience and exchange knowledge by asking or answering questions. Experts in discussion groups can earn a lot of prestige and sometimes economic interests by answering questions [18]. A newly joined user such communities, has no idea about how to ask an appropriate or to search these large questions/answers archives to retrieve high quality content.
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