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TitlePost-Modern Portfolio Theory for Information Retrieval
CreatorTsai, Ming-Feng;Wang, Chuan-Ju
蔡銘峰
Contributor資科系
Key WordsRetrieval models; Optimization; Semivariance
Date2012
Date Issued24-Aug-2015 15:19:59 (UTC+8)
SummaryInformation 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.
RelationProcedia Computer Science, 13, 80-85
Typearticle
DOI http://dx.doi.org/10.1016/j.procs.2012.09.116
dc.contributor 資科系
dc.creator (作者) Tsai, Ming-Feng;Wang, Chuan-Ju
dc.creator (作者) 蔡銘峰zh_TW
dc.date (日期) 2012
dc.date.accessioned 24-Aug-2015 15:19:59 (UTC+8)-
dc.date.available 24-Aug-2015 15:19:59 (UTC+8)-
dc.date.issued (上傳時間) 24-Aug-2015 15:19:59 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/77972-
dc.description.abstract (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.
dc.format.extent 341624 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Procedia Computer Science, 13, 80-85
dc.subject (關鍵詞) Retrieval models; Optimization; Semivariance
dc.title (題名) Post-Modern Portfolio Theory for Information Retrieval
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.procs.2012.09.116
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.procs.2012.09.116