IR-Based Expert Finding Using Filtered Collection

Existing IR-based expert finding generally follows two methods, i.e. the proflfe-based method and the voting-based one. However, neither the expert-relevant data collected in the proflfe- based method nor the query-refevant data used for the voting- based method is compietefy accurate within the confines of current refevance ranking approaches. This problem has been rarely discussed, but impedes expert finding. On this issue, we provide a feasibie sofution, that is, the coifection can be flftered to generate a subset of high-precision reievant data for further processing. In this paper, we propose two perspectives of filtering approaches, i.e. the query-centered perspective and the expert-centered one. For both perspectives, some specific strategies are also discussed and experimented under the CERC collection using the TMJAC model, a voting-based method. On such basis, the different preferences of two perspectives are revealed. Further, to examine the stability of filtering, we examine the filtering strategies using a profile-based method and also testify the effects under the W3C collection. In conclusion, the filtering we proposed is a universal approach of improving expert finding performance.