University rankings generally present users with the problem of placing the results given for an institution in context. Only a comparison with the performance of all other institutions makes it possible to say exactly where an institution stands. In order to interpret the results of the SCImago Institutions Ranking (based on Scopus data) and the Leiden Ranking (based on Web of Science data), i...

Sponsored search is an indispensable business model and a major revenue contributor of almost all the search engines. From the advertisers’ side, participating in ranking the search results by paying for the sponsored search advertisement to aŠract more awareness and purchase facilitates their commercial goal. From the users’ side, presenting personalized advertisement reƒecting their propensit...

This paper presents a novel ranking method for complex semantic relationship (semantic association) search based on user preferences. Our method employs a learning-to-rank algorithm to capture each user's preferences. Using this, it automatically constructs a personalized ranking function for the user. The ranking function is then used to sort the results of each subsequent query by the user. Q...

Ranking functions used in information retrieval are primarily used in the search engines and they are often adopted for various language processing applications. However, features used in the construction of ranking functions should be analyzed before applying it on a data set. This paper gives guidelines on construction of generalized ranking functions with applicationdependent features. The p...

This paper presents theoretical analysis on the generalization ability of listwise learning-to-rank algorithms using Rademacher Average. The paper first proposes a theoretical framework for ranking and then proves a theorem which gives a generalization bound to a listwise ranking algorithm based on Rademacher Average of the class of compound functions operating on the corresponding listwise los...

Ranking theory delivers an account of iterated contraction; each ranking function induces a specific iterated contraction behavior. The paper gives a complete axiomatization of that behavior, i.e., a complete set of laws of iterated contraction. It does so by showing how to reconstruct a ranking function from its iterated contraction behavior uniquely up to multiplicative constant and thus how ...

Ranking theory delivers an account of iterated contraction; each ranking function induces a specific iterated contraction behavior. The paper shows how to reconstruct a ranking function from its iterated contraction behavior uniquely up to multiplicative constant and thus how to measure ranks on a ratio scale. Thereby, it also shows how to completely axiomatize that behavior. The complete set o...

This paper outlines a feasible approach to ranking Grid resources based on an easily obtainable application-specific performance model utilizing low-level performance metrics. First, Grid resources are characterized using lowlevel performance metrics; Then the performance of a given application is associated to the low-level performance measurements via a Ranking Function; Finally, the Ranking ...

The area under an ROC curve (AUC) has been advocated as an evaluation criterion for bipartite ranking problems. In this paper, we study large deviation properties of the AUC; in particular, we derive a distribution-free large deviation bound for the AUC which serves to bound the expected accuracy of a ranking function in terms of its empirical AUC on an independent test sequence.1 A comparison ...

The area under the ROC curve (AUC) has been advocated as an evaluation criterion for the bipartite ranking problem. We study large deviation properties of the AUC; in particular, we derive a distribution-free large deviation bound for the AUC which serves to bound the expected accuracy of a ranking function in terms of its empirical AUC on an independent test sequence. A comparison of our resul...