Portfolio theory of information retrieval
Portfolio theory of information retrieval
复制标题
DOI：
10.1145/1571941.1571963
复制
发表时间：
2009-07
期刊：
影响因子：
--
通讯作者：
Jun Wang;Jianhan Zhu
中科院分区：
文献类型：
--
作者：
Jun Wang;Jianhan Zhu
本文研究了不确定条件下的文档排序问题。它是在个别文件的相关性预测具有不确定性并相互依赖的一般情况下解决的。受现代投资组合理论(一种研究金融市场投资的经济理论)的启发，我们认为，在不确定情况下进行排名不仅仅是选择单个相关文件，而是选择正确的相关文件组合。这促使我们根据文件的预期总体相关性(平均值)及其方差来量化文件的排序列表；后者用作风险的衡量标准，过去很少对文件排序进行研究。通过对均值和方差的分析，我们证明了最优排序是在排序列表的总体相关性(均值)和风险水平(方差)之间取得平衡的排序。在此基础上，提出了一种高效的文档排序算法。它通过同时考虑相关性预测的不确定性和检索文档之间的相关性来推广众所周知的概率排序原则(PRP)。此外，多样化的好处是数学上量化的；我们表明，文档多样化是降低文档排名风险的有效方法。在文本检索中的实验结果证实了该方法的有效性。
This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are dependent between each other. Inspired by the Modern Portfolio Theory, an economic theory dealing with investment in financial markets, we argue that ranking under uncertainty is not just about picking individual relevant documents, but about choosing the right combination of relevant documents. This motivates us to quantify a ranked list of documents on the basis of its expected overall relevance (mean) and its variance; the latter serves as a measure of risk, which was rarely studied for document ranking in the past. Through the analysis of the mean and variance, we show that an optimal rank order is the one that balancing the overall relevance (mean) of the ranked list against its risk level (variance). Based on this principle, we then derive an efficient document ranking algorithm. It generalizes the well-known probability ranking principle (PRP) by considering both the uncertainty of relevance predictions and correlations between retrieved documents. Moreover, the benefit of diversification is mathematically quantified; we show that diversifying documents is an effective way to reduce the risk of document ranking. Experimental results in text retrieval confirm performance.