Uncertainty in Artificial Intelligence
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Position-Aware ListMLE: A Sequential Learning Process for Ranking
Yanyan Lan, Yadong Zhu, Jiafeng Guo, Shuzi Niu, Xueqi Cheng
Abstract:
ListMLE is a state-of-the-art listwise learning-to- rank algorithm, which has been shown to work very well in application. It defines the probabil- ity distribution based on Plackett-Luce Model in a top-down style to take into account the position information. However, both empirical contradic- tion and theoretical results indicate that ListM- LE cannot well capture the position importance, which is a key factor in ranking. To amend the problem, this paper proposes a new listwise rank- ing method, called position-aware ListMLE (p- ListMLE for short). It views the ranking prob- lem as a sequential learning process, with each step learning a subset of parameters which maxi- mize the corresponding stepwise probability dis- tribution. To solve this sequential multi-objective optimization problem, we propose to use lin- ear scalarization strategy to transform it into a single-objective optimization problem, which is efficient for computation. Our theoretical s- tudy shows that p-ListMLE is better than ListM- LE in statistical consistency with respect to typi- cal ranking evaluation measure NDCG. Further- more, our experiments on benchmark datasets demonstrate that the proposed method can sig- nificantly improve the performance of ListMLE and outperform state-of-the-art listwise learning- to-rank algorithms as well.
Keywords:
Pages: 449-458
PS Link:
PDF Link: /papers/14/p449-lan.pdf
BibTex:
@INPROCEEDINGS{Lan14,
AUTHOR = "Yanyan Lan and Yadong Zhu and Jiafeng Guo and Shuzi Niu and Xueqi Cheng",
TITLE = "Position-Aware ListMLE: A Sequential Learning Process for Ranking",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "449--458"
}


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