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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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Journal ArticleDOI
TL;DR: This approach is not suited to log-linear probabilistic models and it needs large samples of relevance feedback data for its application, but it can handle very complex representations of documents and requests and it can be easily applied to multivalued relevance scales.
Abstract: We show that any approach to developing optimum retrieval functions is based on two kinds of assumptions: first, a certain form of representation for documents and requests, and second, additional simplifying assumptions that predefine the type of the retrieval function. Then we describe an approach for the development of optimum polynomial retrieval functions: request-document pairs (fl, dm) are mapped onto description vectors x(fl, dm), and a polynomial function e(x) is developed such that it yields estimates of the probability of relevance P(R | x (fl, dm) with minimum square errors. We give experimental results for the application of this approach to documents with weighted indexing as well as to documents with complex representations. In contrast to other probabilistic models, our approach yields estimates of the actual probabilities, it can handle very complex representations of documents and requests, and it can be easily applied to multivalued relevance scales. On the other hand, this approach is not suited to log-linear probabilistic models and it needs large samples of relevance feedback data for its application.

160 citations

Patent
07 Mar 2008
TL;DR: In this article, a system is provided to improve the relevance of information searches, which includes a search component to facilitate information retrieval in response to a user's query, and an inference component refines the user query or filters search results associated with the query in view of a determined intent of the user.
Abstract: A system is provided to improve the relevance of information searches. The system includes a search component to facilitate information retrieval in response to a user's query. An inference component refines the user's query or filters search results associated with the query in view of a determined intent of the user. This can also include a “sensor component” that collects the information fed to the inference component.

160 citations

Patent
01 May 2007
TL;DR: In this paper, a method and apparatus for utilizing user behavior to immediately modify sets of search results so that the most relevant documents are moved to the top of the search results is presented.
Abstract: A method and apparatus for utilizing user behavior to immediately modify sets of search results so that the most relevant documents are moved to the top. In one embodiment of the invention, behavior data, which can come from virtually any activity, is used to infer the user's intent. The updated inferred implicit user model is then exploited immediately by re-ranking the set of matched documents to best reflect the information need of the user. The system updates the user model and immediately re-ranks documents at every opportunity in order to constantly provide the most optimal results. In another embodiment, the system determines, based on the similarity of results sets, if the current query belongs in the same information session as one or more previous queries. If so, the current query is expanded with additional keywords in order to improve the targeting of the results.

159 citations

Journal ArticleDOI
TL;DR: Methods allowing users to query and explore results which have been relevance‐ranked in terms of both thematic and spatial relevance have been implanted and a usability study indicates that users are happy with the range of spatial relationships available and intuitively understand how to use such a search engine.
Abstract: Much of the information stored on the web contains geographical context, but current search engines treat such context in the same way as all other content. In this paper we describe the design, implementation and evaluation of a spatially aware search engine which is capable of handling queries in the form of the triplet of 〈theme〉〈spatial relationship〉〈location〉. The process of identifying geographic references in documents and assigning appropriate footprints to documents, to be stored together with document terms in an appropriate indexing structure allowing real-time search, is described. Methods allowing users to query and explore results which have been relevance-ranked in terms of both thematic and spatial relevance have been implanted and a usability study indicates that users are happy with the range of spatial relationships available and intuitively understand how to use such a search engine. Normalised precision for 38 queries, containing four types of spatial relationships, is significantly higher (p<0.001) for searches exploiting spatial information than pure text search.

159 citations

Proceedings ArticleDOI
18 May 2013
TL;DR: This paper proposes TagCombine, an automatic tag recommendation method which analyzes objects in software information sites and recommends tags after analyzing the terms in the objects.
Abstract: Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance and test processes as software information sites. It is common to see tags in software information sites and many sites allow users to tag various objects with their own words. Users increasingly use tags to describe the most important features of their posted contents or projects. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has 3 different components: 1. multilabel ranking component which considers tag recommendation as a multi-label learning problem; 2. similarity based ranking component which recommends tags from similar objects; 3. tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on 2 software information sites, StackOverflow and Freecode, which contain 47,668 and 39,231 text documents, respectively, and 437 and 243 tags, respectively. Experiment results show that for StackOverflow, our TagCombine achieves recall@5 and recall@10 scores of 0.5964 and 0.7239, respectively; For Freecode, it achieves recall@5 and recall@10 scores of 0.6391 and 0.7773, respectively. Moreover, averaging over StackOverflow and Freecode results, we improve TagRec proposed by Al-Kofahi et al. by 22.65% and 14.95%, and the tag recommendation method proposed by Zangerle et al. by 18.5% and 7.35% for recall@5 and recall@10 scores.

159 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