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Proceedings.mlr.pres S/v28/wauthier13.pdf

wauthier13.pdf
Preview of proceedings.mlr.pres s/v28/wauthier13.pdf
🔗 Source: proceedings.mlr.press
📊 Size: 469 KB
👤 Author: Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic
⬇️ Downloads: 287

Summary

The ranking of n objects based on pairwise comparisons is a core machine learning problem, arising in recommender systems, ad placement, player ranking, biological applications, and others. In many practical situations, the true pairwise comparisons cannot be actively measured, but a subset of all n(n−1)/2 comparisons is passively and noisily observed. Optimization algorithms could be used to predict a ranking with fixed expected Kendall tau distance, while achieving an Ω(n) lower bound on the corresponding sample complexity. However, due to their centralized structure, they are difficult to extend to online or distributed settings. Two simple algorithms are presented, which match the same Ω(n) lower bound in expectation, and with slightly more measurements, can recover the true ranking in a uniform sense or predict the ranking more accurately near the top than the bottom. The algorithms are easier to extend to online and distributed learning, with benefits over traditional alternatives.

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The ranking of n objects based on pairwise comparisons is a core machine learning problem, arising in recommender systems, ad placement, player ranking,...

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  • File Format: PDF
  • File Size: 469 KB
  • Pages: 9
  • Language: EN
  • Author: Fabian L. Wauthier, Michael I. Jordan, Nebojsa Jojic
  • Total Downloads: 287
  • Last Updated: 3 weeks ago

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