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TournaRank: When retrieval becomes document competition

机译：TournaRank：当检索成为文档竞争时

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摘要

Numerous feature-based models have been recently proposed by the information retrieval community. The capability of features to express different relevance facets (query- or document-dependent) can explain such a success story. Such models are most of the time supervised, thus requiring a learning phase. To leverage the advantages of feature-based representations of documents, we proposeTournaRank, an unsupervised approach inspired by real-life game and sport competition principles. Documents compete against each other in tournaments using features as evidences of relevance. Tournaments are modeled as a sequence of matches, which involve pairs of documents playing in turn their features. Once a tournament is ended, documents are ranked according to their number of won matches during the tournament. This principle is generic since it can be applied to any collection type. It also provides great flexibility since different alternatives can be considered by changing the tournament type, the match rules, the feature set, or the strategies adopted by documents during matches.TournaRankwas experimented on several collections to evaluate our model in different contexts and to compare it with related approaches such as Learning To Rank and fusion ones: the TREC Robust2004 collection for homogeneous documents, the TREC Web2014 (ClueWeb12) collection for heterogeneous web documents, and the LETOR3.0 collection for comparison with supervised feature-based models.
机译：信息检索社区最近提出了许多基于特征的模型。功能表达不同相关方面（与查询或文档相关）的能力可以解释这种成功案例。大部分时间都在监督此类模型，因此需要学习阶段。为了利用基于特征的文档表示的优势，我们提出了TournaRank，这是一种不受监督的方法，受现实游戏和体育竞赛原则的启发。文档在比赛中使用功能作为相关证据相互竞争。比赛被建模为一系列比赛，其中包括成对的文件，依次播放其功能。比赛结束后，将根据文件在比赛中赢得比赛的次数对文件进行排名。该原理是通用的，因为它可以应用于任何集合类型。它还提供了极大的灵活性，因为可以通过更改比赛类型，比赛规则，功能集或比赛中文档采用的策略来考虑不同的选择。TournaRank在多个集合上进行了实验，以在不同的环境中评估我们的模型并进行比较相关方法，例如学习排名和融合方法：用于同类文档的TREC Robust2004集合，用于异构Web文档的TREC Web2014（ClueWeb12）集合以及用于与基于监督的基于特征的模型进行比较的LETOR3.0集合。

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