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    题名: Post-Modern Portfolio Theory for Information Retrieval
    作者: Tsai, Ming-Feng;Wang, Chuan-Ju
    蔡銘峰
    贡献者: 資科系
    关键词: Retrieval models;Optimization;Semivariance
    日期: 2012
    上传时间: 2015-08-24 15:19:59 (UTC+8)
    摘要: 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.
    關聯: Procedia Computer Science, 13, 80-85
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1016/j.procs.2012.09.116
    DOI: 10.1016/j.procs.2012.09.116
    显示于类别:[資訊科學系] 期刊論文

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