Supervised approaches for explicit search result diversification

•We leverage supervised learning methods to improve the effectiveness of explicit search result diversification.•We cast the diversification problem as that of learning a ranking model, based on the coverage of query aspects by each candidate document.•We learn the importance of query aspects by re-ranking the candidate documents for each aspect and leveraging query performance predictors.•We cast the diversification problem as a fusion task, namely, the supervised merging of rankings per query aspect.