  The emergence of online enterprises spread across continents have given rise
to the need for expert identification in this domain. Scenarios that includes
the intention of the employer to find tacit expertise and knowledge of an
employee that is not documented or self-disclosed has been addressed in this
article. The existing reputation based approaches towards expertise ranking in
enterprises utilize PageRank, normal distribution, and hidden Markov model for
expertise ranking. These models suffer issue of negative referral, collusion,
reputation inflation, and dynamism. The authors have however proposed a
Bayesian approach utilizing beta probability distribution based reputation
model for employee ranking in enterprises. The experimental results reveal
improved performance compared to previous techniques in terms of Precision and
Mean Average Error (MAE) with almost 7% improvement in precision on average for
the three data sets. The proposed technique is able to differentiate categories
of interactions in a dynamic context. The results reveal that the technique is
independent of the rating pattern and density of data.
