Learning to rank with multiple objective functions

We investigate the problem of learning to rank for document retrieval from the perspective of learning with multiple objective functions. We present solutions to two open problems in learning to rank: first, we show how multiple measures can be combined into a single graded measure that can be learned. This solves the problem of learning from a ‘scorecard’ of measures by making such scorecards comparable, and we show results where a standard web relevance measure (NDCG) is used for the top-tier measure, and a relevance measure derived from click data is used for the second-tier measure; the second-tier measure is shown to significantly improve while leaving the top-tier measure largely unchanged. Second, we note that the learning-to-rank problem can itself be viewed as changing as the ranking model learns: for example, early in learning, adjusting the rank of all documents can be advantageous, but later during training, it becomes more desirable to concentrate on correcting the ...

• Type: Journal
• Year: 2011
• Where: WWW
• Authors: Krysta Marie Svore, Maksims Volkovs, Christopher J. C. Burges
• Keywords: Internet Technology, Relevance Measures, Second-tier Measure, Top-tier Measure, WWW 2011