Multi-objective optimization in learning to rank.

Supervised learning to rank algorithms typically optimize for high relevance and ignore other facets of search quality, such as freshness and diversity. Prior work on multi-objective ranking trained rankers focused on using hybrid labels that combine overall quality of documents, and implicitly incorporate multiple criteria into quantifying ranking risks. However, these hybrid scores are usually generated based on heuristics without considering potential correlations between individual facets (e.g., freshness versus relevance). In this poster, we empirically demonstrate that the correlation between objective facets in multi-criteria ranking optimization may significantly influence the effectiveness of trained rankers with respect to each objective.