Multi-objective optimization in learning to rank

Authors:
Na Dai;Milad Shokouhi;Brian D. Davison
Affiliations:
Lehigh University, Bethlehem, PA, USA;Microsoft Research, Cambridge, United Kingdom;Lehigh University, Bethlehem, PA, USA
Venue:
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Year:
2011

Citing 8
Cited 0

IR evaluation methods for retrieving highly relevant documents

SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An outranking approach for rank aggregation in information retrieval

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Analysis and Classification of Multi-Criteria Recommender Systems

World Wide Web
User-centric multi-criteria information retrieval

Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Towards recency ranking in web search

Proceedings of the third ACM international conference on Web search and data mining
Learning to efficiently rank

Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Learning to rank for freshness and relevance

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.