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    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/77972
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/77972


    Title: Post-Modern Portfolio Theory for Information Retrieval
    Authors: Tsai, Ming-Feng;Wang, Chuan-Ju
    蔡銘峰
    Contributors: 資科系
    Keywords: Retrieval models;Optimization;Semivariance
    Date: 2012
    Issue Date: 2015-08-24 15:19:59 (UTC+8)
    Abstract: Information Retrieval (IR) aims to discover relevant information according to a user's information need. The classic Probability Ranking Principle (PRP) forms the theoretical basis for probabilistic IR models. This ranking principle, however, neglects the uncertainty introduced through the estimations from retrieval models. Inspired by the Post-Modern Portfolio Theory (PMPT), this paper proposes a mean-semivariance framework to handle the uncertainty. The proposed framework not only deals with the uncertainty but has the ability to distinguish bad surprises (downside uncertainty) and good surprises (upside uncertainty) when optimizing a ranking list. The experimental results shows that the proposed method improves the IR performance over the PRP baseline in terms of most of IR evaluation metrics; moreover, the results suggest that the mean-semivariance framework can further boost the top-position ranking quality.
    Relation: Procedia Computer Science, 13, 80-85
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.procs.2012.09.116
    DOI: 10.1016/j.procs.2012.09.116
    Appears in Collections:[資訊科學系] 期刊論文

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