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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


Papers
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Proceedings ArticleDOI
23 Jul 2007
TL;DR: It is found empirically that, when γ = 1, DiffusionRank has a Penicillin-like effect on the link manipulation, and the algorithm can be employed to find group-to-group relations on the Web, to divide the Web graph into several parts, and to find link communities.
Abstract: While the PageRank algorithm has proven to be very effective for ranking Web pages, the rank scores of Web pages can be manipulated. To handle the manipulation problem and to cast a new insight on the Web structure, we propose a ranking algorithm called DiffusionRank. DiffusionRank is motivated by the heat diffusion phenomena, which can be connected to Web ranking because the activities flow on the Web can be imagined as heat flow, the link from a page to another can be treated as the pipe of an air-conditioner, and heat flow can embody the structure of the underlying Web graph. Theoretically we show that DiffusionRank can serve as a generalization of PageRank when the heat diffusion co-efficient γ tends to infinity. In such a case 1=γ= 0, DiffusionRank (PageRank) has low ability of anti-manipulation. When γ = 0, DiffusionRank obtains the highest ability of anti-manipulation, but in such a case, the web structure is completely ignored. Consequently, γ is an interesting factor that can control the balance between the ability of preserving the original Web and the ability of reducing the effect of manipulation. It is found empirically that, when γ = 1, DiffusionRank has a Penicillin-like effect on the link manipulation. Moreover, DiffusionRank can be employed to find group-to-group relations on the Web, to divide the Web graph into several parts, and to find link communities. Experimental results show that the DiffusionRank algorithm achieves the above mentioned advantages as expected.

102 citations

Proceedings ArticleDOI
20 Aug 2006
TL;DR: A framework for ranking networked entities based on Markov walks with parameterized conductance values associated with the network edges and a constrained maximum entropy network flow formulation whose dual can be solved efficiently using a cutting-plane approach and a quasi-Newton optimizer.
Abstract: Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections or relations between entities. Meanwhile, Pagerank and variants find the stationary distribution of a reasonable but arbitrary Markov walk over a network, but do not learn from relevance feedback. We present a framework for ranking networked entities based on Markov walks with parameterized conductance values associated with the network edges. We propose two flavors of conductance learning problems in our framework. In the first setting, relevance feedback comparing node-pairs hints that the user has one or more hidden preferred communities with large edge conductance, and the algorithm must discover these communities. We present a constrained maximum entropy network flow formulation whose dual can be solved efficiently using a cutting-plane approach and a quasi-Newton optimizer. In the second setting, edges have types, and relevance feedback hints that each edge type has a potentially different conductance, but this is fixed across the whole network. Our algorithm learns the conductances using an approximate Newton method.

102 citations

Patent
11 Apr 2006
TL;DR: In this article, a method and system for dynamically refining and navigating between alternative search query elements are disclosed, which is applicable to searching an information system such as the Internet, an intranet, or any database, lexicon, or collection of documents, disk drive, images or video or audio content.
Abstract: A method and system for dynamically refining and navigating between alternative search query elements are disclosed. The method and system are applicable to searching an information system such as the Internet, an intranet, or any database, lexicon, or collection of documents, disk drive, images or video or audio content. A user enters their search query into a search query receiver. As the user enters their search query, they see, in real-time in a dynamically-generated object, such as a drop-down menu, iFrame, or browser window, possible matches to their search query string, and more specifically, the user receives within the dynamic object alternative semantically- and lexically-related search elements that relate to the search query string and from which the user can either make a selection to further refine their search query, or the user can proceed to view search results based on the selected query element. The relation of alternate lexical elements is based on a controlled or structured vocabulary (for example a thesaurus).

102 citations

Book ChapterDOI
22 Aug 2005
TL;DR: This work proposes adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conducts an extensive experimental study, which evaluates the effectiveness of proposed solutions.
Abstract: We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.

102 citations

Proceedings ArticleDOI
11 Aug 2002
TL;DR: This work describes an automatic query refinement technique, which focuses on improving precision of the top ranked documents, using lexical affinities (LAs), pairs of closely related words which contain exactly one of the original query terms.
Abstract: This work describes an automatic query refinement technique, which focuses on improving precision of the top ranked documents. The terms used for refinement are lexical affinities (LAs), pairs of closely related words which contain exactly one of the original query terms. Adding these terms to the query is equivalent to re-ranking search results, thus, precision is improved while recall is preserved. We describe a novel method that selects the most "informative" LAs for refinement, namely, those LAs that best separate relevant documents from irrelevant documents in the set of results. The information gain of candidate LAs is determined using unsupervised estimation that is based on the scoring function of the search engine. This method is thus fully automatic and its quality depends on the quality of the scoring function. Experiments we conducted with TREC data clearly show a significant improvement in the precision of the top ranked documents.

102 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168