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Rank Aggregation Methods for the Web
, 2010
"... We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building metasearch engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. Wed ..."
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Cited by 478 (6 self)
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We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building metasearch engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. Wedevelop a set of techniques for the rank aggregation problem and compare their performance to that of wellknown methods. A primary goal of our work is to design rank aggregation techniques that can effectively combat "spam," a serious problem in Web searches. Experiments show that our methods are simple, efficient, and effective.
A Survey of Topk Query Processing Techniques in Relational Database Systems
"... Efficient processing of topk queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient topk processing in domains such as the Web, multimedia search and distributed systems has shown a great impact on performance. In this surve ..."
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Cited by 167 (6 self)
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Efficient processing of topk queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient topk processing in domains such as the Web, multimedia search and distributed systems has shown a great impact on performance. In this survey, we describe and classify topk processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss topk queries in XML domain, and show their connections to relational approaches.
Efficient Similarity Search and Classification Via Rank Aggregation
 In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data
, 2003
"... We propose a novel approach to performing efficient similarity search and classification in high dimensional data. In this framework, the database elements are vectors in a Euclidean space. Given a query vector in the same space, the goal is to find elements of the database that are similar to the ..."
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Cited by 152 (3 self)
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We propose a novel approach to performing efficient similarity search and classification in high dimensional data. In this framework, the database elements are vectors in a Euclidean space. Given a query vector in the same space, the goal is to find elements of the database that are similar to the query. In our approach, a small number of independent "voters" rank the database elements based on similarity to the query. These rankings are then combined by a highly efficient aggregation algorithm. Our methodology leads both to techniques for computing approximate nearest neighbors and to a conceptually rich alternative to nearest neighbors.
Preference Functions That Score Rankings and Maximum Likelihood Estimation
"... A preference function (PF) takes a set of votes (linear orders over a set of alternatives) as input, and produces one or more rankings (also linear orders over the alternatives) as output. Such functions have many applications, for example, aggregating the preferences of multiple agents, or merging ..."
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Cited by 62 (20 self)
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A preference function (PF) takes a set of votes (linear orders over a set of alternatives) as input, and produces one or more rankings (also linear orders over the alternatives) as output. Such functions have many applications, for example, aggregating the preferences of multiple agents, or merging rankings (of, say, webpages) into a single ranking. The key issue is choosing a PF to use. One natural and previously studied approach is to assume that there is an unobserved “correct ” ranking, and the votes are noisy estimates of this. Then, we can use the PF that always chooses the maximum likelihood estimate (MLE) of the correct ranking. In this paper, we define simple ranking scoring functions (SRSFs) and show that the class of neutral SRSFs is exactly the class of neutral PFs that are MLEs for some noise model. We also define extended ranking scoring functions (ERSFs) and show a condition under which these coincide with SRSFs. We study key properties such as consistency and continuity, and consider some example PFs. In particular, we study Single Transferable Vote (STV), a commonly used PF, showing that it is an ERSF but not an SRSF, thereby clarifying the extent to which it is an MLE function. This also gives a new perspective on how ties should be broken under STV. We leave some open questions. 1
How Hard Is Bribery in Elections?
"... We study the complexity of influencing elections through bribery: How computationally complex is it for an external actor to determine whether by paying certain voters to change their preferences a specified candidate can be made the election’s winner? We study this problem for election systems as v ..."
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Cited by 56 (23 self)
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We study the complexity of influencing elections through bribery: How computationally complex is it for an external actor to determine whether by paying certain voters to change their preferences a specified candidate can be made the election’s winner? We study this problem for election systems as varied as scoring protocols and Dodgson voting, and in a variety of settings regarding homogeneousvs.nonhomogeneous electorate bribability, boundedsizevs.arbitrarysized candidate sets, weightedvs.unweighted voters, and succinctvs.nonsuccinct input specification. We obtain both polynomialtime bribery algorithms and proofs of the intractability of bribery, and indeed our results show that the complexity of bribery is extremely sensitive to the setting. For example, we find settings in which bribery is NPcomplete but manipulation (by voters) is in P, and we find settings in which bribing weighted voters is NPcomplete but bribing voters with individual bribe thresholds is in P. For the broad class of elections (including plurality, Borda, kapproval, and veto) known as scoring protocols, we prove a dichotomy result for bribery of weighted voters: We find a simpletoevaluate condition that classifies every case as either NPcomplete or in P. 1.
Statistical ranking and combinatorial Hodge theory
 Mathematical Programming
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Multimode Control Attacks on Elections
"... In 1992, Bartholdi, Tovey, and Trick [1992] opened the study of control attacks on elections—attempts to improve the election outcome by such actions as adding/deleting candidates or voters. That work has led to many results on how algorithms can be used to find attacks on elections and how complexi ..."
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Cited by 33 (12 self)
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In 1992, Bartholdi, Tovey, and Trick [1992] opened the study of control attacks on elections—attempts to improve the election outcome by such actions as adding/deleting candidates or voters. That work has led to many results on how algorithms can be used to find attacks on elections and how complexitytheoretic hardness results can be used as shields against attacks. However, all the work in this line has assumed that the attacker employs just a single type of attack. In this paper, we model and study the case in which the attacker launches a multipronged (i.e., multimode) attack. We do so to more realistically capture the richness of reallife settings. For example, an attacker might simultaneously try to suppress some voters, attract new voters into the election, and introduce a spoiler candidate. Our model provides a unified framework for such varied attacks, and by constructing polynomialtime multiprong attack algorithms we prove that for various election systems even such concerted, flexible attacks can be perfectly planned in deterministic polynomial time. 1
Rank aggregation revisited
"... The rank aggregation problem is to combine many different rank orderings on the same set of candidates, or alternatives, in order to obtain a “better” ordering. Rank aggregation has been studied extensively in the context of social choice theory, where several “voting paradoxes ” have been discovere ..."
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Cited by 23 (0 self)
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The rank aggregation problem is to combine many different rank orderings on the same set of candidates, or alternatives, in order to obtain a “better” ordering. Rank aggregation has been studied extensively in the context of social choice theory, where several “voting paradoxes ” have been discovered. The problem also arises in many other settings: Sports and Competition: How to determine the winner of a season, how to rank players or how to compare players from different eras? Machine Learning: Collaborative filtering and metasearch;
The Complexity of Computing Medians of Relations
, 1998
"... Let N be a finite set and R be the set of all binary relations on N . Consider R endowed with a metric d, the symmetric difference distance. For a given mtuple = (R 1 ; : : : ; Rm ) 2 R m , a relation R 2 R that minimizes the function P m k=1 d(R k ; R) is called a median relation of . In the socia ..."
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Cited by 22 (0 self)
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Let N be a finite set and R be the set of all binary relations on N . Consider R endowed with a metric d, the symmetric difference distance. For a given mtuple = (R 1 ; : : : ; Rm ) 2 R m , a relation R 2 R that minimizes the function P m k=1 d(R k ; R) is called a median relation of . In the social sciences, in qualitative data analysis and in multicriteria decision making, problems occur in which the mtuple represents collected data (preferences, similarities, games) and the objective is that of finding a median relation of with some special feature (representing for example, consensus of preferences, clustering of similar objects, ranking of teams, etc.). In this paper we analyse the computational complexity of all such problems in which the median is required to satisfy one or more of the properties: reexitivity, symmetry, antisymmetry, transitivity and completeness. We prove that whenever transitivity is required (except when symmetry and completeness are also si...