Rank Aggregation: Together We're Strong

Schalekamp, Frans; Zuylen, Anke van

doi:10.1137/1.9781611972894.4

Cited by 87 publications

(117 citation statements)

References 18 publications

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“…The rank aggregation problem can be formulated as an integer linear program (ILP) [5]. The relaxed LP is a 4/3 approximation and empirically often yields integral solutions [20]. In addition, an algorithm based on A * search has been shown to run in O(m 2 ) time on inputs where there is strong agreement among rankings [18].…”

Section: Optimal (Non-private) Rank Aggregationmentioning

confidence: 99%

“…In addition, an algorithm based on A * search has been shown to run in O(m 2 ) time on inputs where there is strong agreement among rankings [18]. Finally, empirical studies show that exact algorithms have reasonably low run times on real and synthetic data [4,20].…”

Section: Optimal (Non-private) Rank Aggregationmentioning

confidence: 99%

“…[3]) with the best approximation being a PTAS [13]. The problem has also been studied empirically, with at least two fairly extensive empirical studies [4,20].…”

Section: Experiments On Synthetic Datasetsmentioning

confidence: 99%

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Differentially Private Rank Aggregation

Hay

Elagina²,

Miklau

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Given a collection of rankings of a set of items, rank aggregation seeks to compute a ranking that can serve as a single best representative of the collection. Rank aggregation is a well-studied problem and a number of effective algorithmic solutions have been proposed in the literature. However, when individuals are asked to contribute a ranking, they may be concerned that their personal preferences will be disclosed inappropriately to others. This acts as a disincentive to individuals to respond honestly in expressing their preferences and impedes data collection and data sharing.We address this problem by investigating rank aggregation under differential privacy, which requires that a released output (here, the aggregate ranking computed from individuals' rankings) remain almost the same if any one individual's ranking is removed from the input. We propose a number of differentially-private rank aggregation algorithms: two are inspired by non-private approximate rank aggregators from the existing literature; another uses a novel rejection sampling method to sample privately from a complex distribution. For all the methods we propose, we quantify, both theoretically and empirically, the "cost" of privacy in terms of the quality of the rank aggregation computed.

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Section: Optimal (Non-private) Rank Aggregationmentioning

confidence: 99%

Section: Optimal (Non-private) Rank Aggregationmentioning

confidence: 99%

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Differentially Private Rank Aggregation

Hay

Elagina²,

Miklau

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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“…Moreover, the index-based approaches can clearly beat even the oracle algorithm Oracle-SubSup, which forms an upper bound on the performance of any aggregation-based technique. For a comparison of the rank aggregation algorithms, the interested reader may refer to Schalekamp and van Zuylen [2009].…”

Section: Semantic Cachingmentioning

confidence: 99%

Cache-Based Query Processing for Search Engines

et al. 2012

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In practice, a search engine may fail to serve a query due to various reasons such as hardware/network failures, excessive query load, lack of matching documents, or service contract limitations (e.g., the query rate limits for third-party users of a search service). In this kind of scenarios, where the backend search system is unable to generate answers to queries, approximate answers can be generated by exploiting the previously computed query results available in the result cache of the search engine. In this work, we propose two alternative strategies to implement this cache-based query processing idea. The first strategy aggregates the results of similar queries that are previously cached in order to create synthetic results for new queries. The second strategy forms an inverted index over the textual information (i.e., query terms and result snippets) present in the result cache and uses this index to answer new queries. Both approaches achieve reasonable result qualities compared to processing queries with an inverted index built on the collection.

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“…Aggregating these rankings into a joint one has been studied as rank aggregation in both machine learning and information retrieval [33]. In this work we propose a probabilistic model for rank aggregation.…”

Section: Aggregating Gene Ranking Listsmentioning

confidence: 99%

An Integrative Approach to Identifying Biologically Relevant Genes

Zhao

Wang

Sharma

et al. 2010

Proceedings of the 2010 SIAM International Conference on Data Mining

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Gene selection aims at detecting biologically relevant genes to assist biologists' research. The cDNA Microarray data used in gene selection is usually "wide". With more than several thousand genes, but only less than a hundred of samples, many biologically irrelevant genes can gain their statistical relevance by sheer randomness. Addressing this problem goes beyond what the cDNA Microarray can offer and necessitates the use of additional information. Recent developments in bioinformatics have made various knowledge sources available, such as the KEGG pathway repository and Gene Ontology database. Integrating different types of knowledge could provide more information about genes and samples. In this work, we propose a novel approach to integrate different types of knowledge for identifying biologically relevant genes. The approach converts different types of external knowledge to its internal knowledge, which can be used to rank genes. Upon obtaining the ranking lists, it aggregates them via a probabilistic model and generates a final list. Experimental results from our study on acute lymphoblastic leukemia demonstrate the efficacy of the proposed approach and show that using different types of knowledge together can help detect biologically relevant genes.

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Rank Aggregation: Together We're Strong

Cited by 87 publications

References 18 publications

Differentially Private Rank Aggregation

Differentially Private Rank Aggregation

Cache-Based Query Processing for Search Engines

An Integrative Approach to Identifying Biologically Relevant Genes

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