Integrating Contextual Factors into Topic-centric Retrieval Models for Finding Similar Experts

Expert finding has been addressed from multiple viewpoints, including expertise seeking and expert retrieval. The focus of expertise seeking has mostly been on descriptive or predictive models, for example to identify what factors affect human decisions on locating and selecting experts. In expert retrieval the focus has been on algorithms similar to document search, which identify topical matches based on the content of documents associated with experts. We report on a pilot study on an expert finding task in which we explore how contextual factors identified by expertise seeking models can be integrated with topic-centric retrieval algorithms and examine whether they can improve retrieval performance for this task. We focus on the task of similar expert finding: given a small number of example experts, find similar experts. Our main finding is that, while topical knowledge is the most important factor, human subjects also consider other factors, such as reliability, upto-dateness, and organizational structure. We find that integrating these factors into topical retrieval models can significantly improve retrieval performance.