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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 work proposes a method of Ranking based Multi-correlation Tensor Factorization (RMTF), to jointly model the ternary relations among user, image, and tag, and further to precisely reconstruct the user-aware image-tag associations as a result.
Abstract: Large-scale user contributed images with tags are easily available on photo sharing websites. However, the noisy or incomplete correspondence between the images and tags prohibits them from being leveraged for precise image retrieval and effective management. To tackle the problem of tag refinement, we propose a method of Ranking based Multi-correlation Tensor Factorization (RMTF), to jointly model the ternary relations among user, image, and tag, and further to precisely reconstruct the user-aware image-tag associations as a result. Since the user interest or background can be explored to eliminate the ambiguity of image tags, the proposed RMTF is believed to be superior to the traditional solutions, which only focus on the binary image-tag relations. During the model estimation, we employ a ranking based optimization scheme to interpret the tagging data, in which the pair-wise qualitative difference between positive and negative examples is used, instead of the point-wise 0/1 confidence. Specifically, the positive examples are directly decided by the observed user-image-tag interrelations, while the negative ones are collected with respect to the most semantically and contextually irrelevant tags. Extensive experiments on a benchmark Flickr dataset demonstrate the effectiveness of the proposed solution for tag refinement. We also show attractive performances on two potential applications as the by-products of the ternary relation analysis.

116 citations

Patent
28 Jun 2006
TL;DR: In this article, a system that incorporates a user context into a computer-based search is provided, which can identify information about a user state or context via a variety of sources and sensors.
Abstract: A system that incorporates a user context into a computer-based search is provided. To establish the context, the innovation can identify information about a user state or context via a variety of sources and sensors. The state/context information can be used to filter, arrange and/or rank search results so as to facilitate converging on meaningful searches and results. Machine learning systems (implicitly and/or explicitly trained) can be employed to infer a current and/or future context related to user. An identified or inferred user context can be employed to modify an automated or user-defined search input/query. Contextual cues can be considered directly in the construction and use of context of context-sensitive retrieval algorithms that are optimized for identifying and/or ranking of informational items of potential interest or value in different contexts. As well, the context can be employed to intelligently render results of a query (e.g., user/application defined, context-modified query).

116 citations

Proceedings ArticleDOI
26 Oct 2008
TL;DR: This paper develops a novel, effective and efficient two-level query suggestion model by mining clickthrough data, in the form of two bipartite graphs (user-query and query-URL bipartITE graphs) extracted from theclickthrough data.
Abstract: For a given query raised by a specific user, the Query Suggestion technique aims to recommend relevant queries which potentially suit the information needs of that user. Due to the complexity of the Web structure and the ambiguity of users' inputs, most of the suggestion algorithms suffer from the problem of poor recommendation accuracy. In this paper, aiming at providing semantically relevant queries for users, we develop a novel, effective and efficient two-level query suggestion model by mining clickthrough data, in the form of two bipartite graphs (user-query and query-URL bipartite graphs) extracted from the clickthrough data. Based on this, we first propose a joint matrix factorization method which utilizes two bipartite graphs to learn the low-rank query latent feature space, and then build a query similarity graph based on the features. After that, we design an online ranking algorithm to propagate similarities on the query similarity graph, and finally recommend latent semantically relevant queries to users. Experimental analysis on the clickthrough data of a commercial search engine shows the effectiveness and the efficiency of our method.

116 citations

Patent
Marion Behnen1, Qi Jin1, Timo Pfahl1, Holger Pirk1
30 Mar 2007
TL;DR: In this paper, the authors present a method for displaying results of a search query in a search interface based on a facet hierarchy of documents that satisfy the query, and a cube structure based on the facet hierarchy and a multi-dimensional search interface.
Abstract: Methods, systems, and computer readable medium for displaying results of a search query. In one implementation, the method includes receiving a query, obtaining documents that satisfy the query, constructing a facet hierarchy based on documents that satisfy the query, creating a cube structure based on the facet hierarchy, and displaying a multi-dimensional search interface based on the cube structure.

116 citations

Proceedings ArticleDOI
26 Apr 2010
TL;DR: A simple framework for classification-enhanced ranking that uses clicks in combination with the classification of web pages to derive a class distribution for the query, which can be used to derive query classes for a variety of different taxonomies.
Abstract: Many have speculated that classifying web pages can improve a search engine's ranking of results. Intuitively results should be more relevant when they match the class of a query. We present a simple framework for classification-enhanced ranking that uses clicks in combination with the classification of web pages to derive a class distribution for the query. We then go on to define a variety of features that capture the match between the class distributions of a web page and a query, the ambiguity of a query, and the coverage of a retrieved result relative to a query's set of classes. Experimental results demonstrate that a ranker learned with these features significantly improves ranking over a competitive baseline. Furthermore, our methodology is agnostic with respect to the classification space and can be used to derive query classes for a variety of different taxonomies.

116 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