Poster (2 pages)
Official ACM published version: http://dx.doi.org/10.1145/2009916.2010139
Author's version: PDF (65KB)
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.
In Proceedings of the 34th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1241-1242, Beijing, China, ACM Press, July 2011.
Back to Brian Davison's publications