An outranking approach for rank aggregation in information retrieval

Farah, Mohamed; Vanderpooten, Daniel

doi:10.1145/1277741.1277843

Cited by 86 publications

(67 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An outranking approach was recently presented in [17]. According to this, a document is ranked better than another if the majority of input rankings is in concordance with this fact and at the same time only a few input rankings refute it.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Top- k dominant web services under multi-criteria matching

Skoutas

Sacharidis

Simitsis

et al. 2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

View full text Add to dashboard Cite

As we move from a Web of data to a Web of services, enhancing the capabilities of the current Web search engines with effective and efficient techniques for Web services retrieval and selection becomes an important issue. Traditionally, the relevance of a Web service advertisement to a service request is determined by computing an overall score that aggregates individual matching scores among the various parameters in their descriptions. Two drawbacks characterize such approaches. First, there is no single matching criterion that is optimal for determining the similarity between parameters. Instead, there are numerous approaches ranging from using Information Retrieval similarity metrics up to semantic logicbased inference rules. Second, the reduction of individual scores to an overall similarity leads to significant information loss. Since there is no consensus on how to weight these scores, existing methods are typically pessimistic, adopting a worst-case scenario. As a consequence, several services, e.g., those having a single unrelated parameter, can be excluded from the result set, even though they are potentially good alternatives. In this work, we present a methodology that overcomes both deficiencies. Given a request, we introduce an objective measure that assigns a dominance score to each advertised Web service. This score takes into consideration all the available criteria for each parameter in the request. We investigate three distinct definitions of dominance score, and we devise efficient algorithms that retrieve the top-k most dominant Web services in each case. Extensive experimental evaluation on real requests and relevance sets, as well as on synthetically generated scenarios, demonstrates both the effectiveness of the proposed technique and the efficiency of the algorithms.Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the ACM. To copy otherwise, or to republish, to post on servers or to redistribute to lists, requires a fee and/or special permissions from the publisher, ACM.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Several methods, reviewed in Section 2, exist for this task. We compare our method to four popular fusion techniques: the score-based approaches CombSum and CombMNZ [18], the simple rank-based method of Borda-fuse [3], and the Outranking approach [17]. The first three techniques are parameter-free.…”

Section: Retrieval Effectivenessmentioning

confidence: 99%

Top- k dominant web services under multi-criteria matching

Skoutas

Sacharidis

Simitsis

et al. 2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

View full text Add to dashboard Cite

show abstract

“…A goal programming method dealing with interval rankings in a group decision-making problem is studied in [13]. Recently, some methods have been applied in the context of meta-search engines ( [9], [25]) and information retrieval [10], among others.…”

Section: Introductionmentioning

confidence: 99%

Rank aggregation methods dealing with incomplete information applied to Smart Cities

Dopazo

Martínez-Cespedes

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

Abstract-City-rankings have become a central instrument for assessing the attractiveness of urban regions over the last years. Demographic, environmental, economic, political and socio-cultural factors are forcing the urban world to design and implement Smart Cities. A set of multidimensional components underlies the fuzzy smart city concept. As a result cities are evaluated and ranked with regard to different characteristics, and smart city measures are achieved through chosen indicators. Therefore, the problem of combining multiple rankings to form an aggregate ranking, which compares city performance, is recognized as a useful tool in this context. Moreover, a usual situation is when incomplete information arises and only partial rankings may be supplied. This paper addresses the general problem of rank aggregation dealing with incomplete information based on rank aggregation methods and multicriteria decision making theory. It consists on constructing a consensus ranking from partial rankings of a set of objects provided according different criteria. Our techniques rely on outranking matrices as a way of collecting relevance information from input data, theory of fuzzy preference relations and the PageRank algorithm.

show abstract

“…The preference between different criteria is quantified by utility measures that affect optimization through preference model representation. The commonly used preference models for search or recommendation tasks include value-focused models [8,9] and outranking relations models [3]. While these approaches exploit the search quality on each aspect (criterionspecific ratings) to enhance overall quality (measurement ratings), they ignore the inter-relationship between different objectives.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization in learning to rank

Nishioka

Shokouhi

Davison

2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

Supervised learning to rank algorithms typically optimize for high relevance and ignore other facets of search quality, such as freshness and diversity. Prior work on multi-objective ranking trained rankers focused on using hybrid labels that combine overall quality of documents, and implicitly incorporate multiple criteria into quantifying ranking risks. However, these hybrid scores are usually generated based on heuristics without considering potential correlations between individual facets (e.g., freshness versus relevance). In this poster, we empirically demonstrate that the correlation between objective facets in multi-criteria ranking optimization may significantly influence the effectiveness of trained rankers with respect to each objective.

show abstract

An outranking approach for rank aggregation in information retrieval

Cited by 86 publications

References 25 publications

Top- k dominant web services under multi-criteria matching

Top- k dominant web services under multi-criteria matching

Rank aggregation methods dealing with incomplete information applied to Smart Cities

Multi-objective optimization in learning to rank

Contact Info

Product

Resources

About