%0 Conference Paper %1 tsai_frank:ranking_2007 %A Tsai, Ming-Feng %A Liu, Tie-Yan %A Qin, Tao %A Chen, Hsin-Hsi %A Ma, Wei-Ying %B Proceedings of the 30th annual ACM SIGIR conference %C Amsterdam, The Netherlands %D 2007 %I ACM %K fidelityloss,frank,learningtorank %P 383--390 %R 10.1145/1277741.1277808 %T FRank: a ranking method with fidelity loss %U http://portal.acm.org/citation.cfm?id=1277741.1277808&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401 %X Ranking problem is becoming important in many fields, especially in information retrieval (IR). Many machine learning techniques have been proposed for ranking problem, such as RankSVM, RankBoost, and RankNet. Among them, RankNet, which is based on a probabilistic ranking framework, is leading to promising results and has been applied to a commercial Web search engine. In this paper we conduct further study on the probabilistic ranking framework and provide a novel loss function named fidelity loss for measuring loss of ranking. The fidelity loss notonly inherits effective properties of the probabilistic ranking framework in RankNet, but possesses new properties that are helpful for ranking. This includes the fidelity loss obtaining zero for each document pair, and having a finite upper bound that is necessary for conducting query-level normalization. We also propose an algorithm named FRank based on a generalized additive model for the sake of minimizing the fedelity loss and learning an effective ranking function. We evaluated the proposed algorithm for two datasets: TREC dataset and real Web search dataset. The experimental results show that the proposed FRank algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search. %@ 978-1-59593-597-7

@inproceedings{tsai_frank:ranking_2007, abstract = {Ranking problem is becoming important in many fields, especially in information retrieval (IR). Many machine learning techniques have been proposed for ranking problem, such as RankSVM, RankBoost, and RankNet. Among them, RankNet, which is based on a probabilistic ranking framework, is leading to promising results and has been applied to a commercial Web search engine. In this paper we conduct further study on the probabilistic ranking framework and provide a novel loss function named fidelity loss for measuring loss of ranking. The fidelity loss notonly inherits effective properties of the probabilistic ranking framework in RankNet, but possesses new properties that are helpful for ranking. This includes the fidelity loss obtaining zero for each document pair, and having a finite upper bound that is necessary for conducting query-level normalization. We also propose an algorithm named FRank based on a generalized additive model for the sake of minimizing the fedelity loss and learning an effective ranking function. We evaluated the proposed algorithm for two datasets: TREC dataset and real Web search dataset. The experimental results show that the proposed FRank algorithm outperforms other learning-based ranking methods on both conventional IR problem and Web search.}, added-at = {2009-03-05T08:50:18.000+0100}, address = {Amsterdam, The Netherlands}, author = {Tsai, Ming-Feng and Liu, Tie-Yan and Qin, Tao and Chen, Hsin-Hsi and Ma, Wei-Ying}, biburl = {https://www.bibsonomy.org/bibtex/27747f7306d70552c586db6774c2caac1/bcao}, booktitle = {Proceedings of the 30th annual ACM SIGIR conference}, doi = {10.1145/1277741.1277808}, interhash = {e70d0c7a5fc734b296ff8236e92d63a6}, intrahash = {7747f7306d70552c586db6774c2caac1}, isbn = {978-1-59593-597-7}, keywords = {fidelityloss,frank,learningtorank}, pages = {383--390}, publisher = {ACM}, timestamp = {2009-03-05T08:50:18.000+0100}, title = {FRank: a ranking method with fidelity loss}, url = {http://portal.acm.org/citation.cfm?id=1277741.1277808\&coll=GUIDE\&dl=GUIDE\&CFID=7216732\&CFTOKEN=41760401}, year = 2007 }