Towards a Multi-Feature Enabled Approach for Optimized Expert Seeking

摘要

With the enormous growth of data, retrieving information from the Web became more desirableand even more challenging because of the Big Data issues (e.g. noise, corruption, badquality…etc.). Expert seeking, defined as returning a ranked list of expert researchers given atopic, has been a real concern in the last 15 years. This kind of task comes in handy whenbuilding scientific committees, requiring to identify the scholars’ experience to assign them themost suitable roles in addition to other factors as well. Due to the fact the Web is drowning withplenty of data, this opens up the opportunity to collect different kinds of expertise evidence. Inthis paper, we propose an expert seeking approach with specifying the most desirable features(i.e. criteria on which researcher’s evaluation is done) along with their estimation techniques.We utilized some machine learning techniques in our system and we aim at verifying theeffectiveness of incorporating influential features that go beyond publications.