Selective collection enrichment in user-centred health information retrieval

Due to the increasing amount of electronic information, a majority of laypeople (ordinary people with no professional medical knowledge) now rely on the Web to seek for health information for self-diagnosis. However, research has shown that current search engines are failing to deliver effective search results due to the inability of laypeople to formulate good queries because of lack of domain knowledge and unfamiliarity with the medical vocabulary and concepts. This article attempts to address this by proposing a Selective Collection Enrichment approach that uses three different external resources to enrich a user query, thus generating three different expanded queries. In addition, we deploy pre-retrieval query performance predictors to select an expanded query that is most likely to perform better when retrieving on a local collection being searched. Furthermore, we evaluate the effects of combining several pre-retrieval query performance predictors' scores using data fusion techniques for Selective Collection Enrichment. Our empirical evaluation shows marked improvement in the retrieval performance in terms of nDCG@10 when the Selective Collection Enrichment approach is deployed.