Ranking Experts with Discriminative Probabilistic Models

In the realistic settings of expert finding, the evidence for expertise often comes from heterogeneous knowledge sources. As some sources tend to be more reliable and indicative than the others, different data sources need to receive different weights to reflect their degrees of importance. However, most previous studies in expert finding did not differentiate data sources, which may lead to unsatisfactory performance in the settings where the heterogeneity of data sources is present. In this paper, we investigate how to merge and weight heterogeneous knowledge sources in the context of expert finding. A relevance-based supervised learning framework is presented to learn the combination weights from training data. Beyond just learning a fixed combination strategy for all the queries and experts, we propose a series of probabilistic models which have increasing capability to associate the combination weights with specific experts and queries. In the last (and also the most sophisticated) proposed model, the combination weights depend on both expert classes and query topics, and these classes and topics are derived from expert and query features. Compared with expert and query independent combination methods, the proposed combination strategy can better adjust to different types of experts and queries. In consequence, the model yields much flexibility of combining data sources when dealing with a broad range of expertise areas and a large variation in experts. Empirical studies on a real world faculty expertise testbed demonstrate the effectiveness and robustness of the proposed learning based models.