2007/04/25 by Quoc V. Le, Quoc Le, Le, Quoc +3
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Data Management and Algorithms #FOS: Computer and information sciences #Game Theory and Voting Systems #Information Retrieval (cs.IR) #Multi-Criteria Decision Making #cs.AI #cs.IR
paper · pdf · doi:10.48550/arxiv.0704.3359
arxiv created 2007/04/25 · openalex publication_date 2007/04/25 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Web page ranking and collaborative filtering require the optimization of sophisticated performance measures. Current Support Vector approaches are unable to optimize them directly and focus on pairwise comparisons instead. We present a new approach which allows direct optimization of the relevant loss functions. This is achieved via structured estimation in Hilbert spaces. It is most related to Max-Margin-Markov networks optimization of multivariate performance measures. Key to our approach is that during training the ranking problem can be viewed as a linear assignment problem, which can be solved by the Hungarian Marriage algorithm. At test time, a sort operation is sufficient, as our algorithm assigns a relevance score to every (document, query) pair. Experiments show that the our algorithm is fast and that it works very well.