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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
04 Feb 2013
TL;DR: In this article, the authors investigate whether and how previously collected (historical) interaction data can be used to speed up learning in online learning to rank for information retrieval (IR).
Abstract: Online learning to rank for information retrieval (IR) holds promise for allowing the development of "self-learning" search engines that can automatically adjust to their users. With the large amount of e.g., click data that can be collected in web search settings, such techniques could enable highly scalable ranking optimization. However, feedback obtained from user interactions is noisy, and developing approaches that can learn from this feedback quickly and reliably is a major challenge.In this paper we investigate whether and how previously collected (historical) interaction data can be used to speed up learning in online learning to rank for IR. We devise the first two methods that can utilize historical data (1) to make feedback available during learning more reliable and (2) to preselect candidate ranking functions to be evaluated in interactions with users of the retrieval system. We evaluate both approaches on 9 learning to rank data sets and find that historical data can speed up learning, leading to substantially and significantly higher online performance. In particular, our pre-selection method proves highly effective at compensating for noise in user feedback. Our results show that historical data can be used to make online learning to rank for IR much more effective than previously possible, especially when feedback is noisy.

121 citations

Proceedings ArticleDOI
20 Apr 2009
TL;DR: A novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members is introduced, exploiting image features known for having significant effects on the visual quality perceived by humans as well as textual meta data.
Abstract: Web 2.0 applications like Flickr, YouTube, or Del.icio.us are increasingly popular online communities for creating, editing and sharing content. The growing size of these folksonomies poses new challenges in terms of search and data mining. In this paper we introduce a novel methodology for automatically ranking and classifying photos according to their attractiveness for folksonomy members. To this end, we exploit image features known for having significant effects on the visual quality perceived by humans (e.g. sharpness and colorfulness) as well as textual meta data, in what is a multi-modal approach. Using feedback and annotations available in the Web 2.0 photo sharing system Flickr, we assign relevance values to the photos and train classification and regression models based on these relevance assignments. With the resulting machine learning models we categorize and rank photos according to their attractiveness. Applications include enhanced ranking functions for search and recommender methods for attractive content. Large scale experiments on a collection of Flickr photos demonstrate the viability of our approach.

121 citations

Journal Article
TL;DR: In this article, the IS manager as coordinator, users and top executives can contribute to an eight-step process that will reconcile differing perspectives and permit an orderly ranking of projects, which helps managers to make a more effective use of IS resources because it includes other elements relative to the priority-setting process.
Abstract: When it comes to deciding which project proposals should get the nod, top executives, information systems managers, and users often have conflicting views. None of these should make the choice alone, says this author. With the IS manager as coordinator, users and top executives can contribute to an eight-step process that will reconcile differing perspectives and permit an orderly ranking of projects. Such a structured approach helps managers to make a more effective use of IS resources because it includes other elements relative to the priority-setting process, rather than just those that are purely financial.

120 citations

Proceedings ArticleDOI
01 Jun 1996
TL;DR: In this article, the authors investigate how to optimize the processing of queries over multimedia repositories and define an execution space that is search-minimal, i.e., the set of indexes searched is minimal.
Abstract: Repositories of multimedia objects having multiple types of attributes (e.g., image, text) are becoming increasingly common. A selection on these attributes will typically produce not just a set of objects, as in the traditional relational query model (filtering), but also a grade of match associated with each object, indicating how well the object matches the selection condition (ranking). Also, multimedia repositories may allow access to the attributes of each object only through indexes. We investigate how to optimize the processing of queries over multimedia repositories. A key issue is the choice of the indexes used to search the repository. We define an execution space that is search-minimal, i.e., the set of indexes searched is minimal. Although the general problem of picking an optimal plan in the search-minimal execution space is NP-hard, we solve the problem efficiently when the predicates in the query are independent. We also show that the problem of optimizing queries that ask for a few top-ranked objects can be viewed, in many cases, as that of evaluating selection conditions. Thus, both problems can be viewed together as an extended filtering problem.

120 citations

Journal IssueDOI
TL;DR: It is shown that a mutual reinforcement relationship between ranking and Web-snippet clustering does exist, and the better the ranking of the underlying search engines, the more relevant the results from which SnakeT distills the hierarchy of labeled folders, and hence the more useful this hierarchy is to the user.
Abstract: We propose a (meta-)search engine, called SnakeT (SNippet Aggregation for Knowledge ExtracTion), which queries more than 18 commodity search engines and offers two complementary views on their returned results. One is the classical flat-ranked list, the other consists of a hierarchical organization of these results into folders created on-the-fly at query time and labeled with intelligible sentences that capture the themes of the results contained in them. Users can browse this hierarchy with various goals: knowledge extraction, query refinement and personalization of search results. In this novel form of personalization, the user is requested to interact with the hierarchy by selecting the folders whose labels (themes) best fit her query needs. SnakeT then personalizes on-the-fly the original ranked list by filtering out those results that do not belong to the selected folders. Consequently, this form of personalization is carried out by the users themselves and thus results fully adaptive, privacy preserving, scalable and non-intrusive for the underlying search engines. We have extensively tested SnakeT and compared it against the best available Web-snippet clustering engines. SnakeT is efficient and effective, and shows that a mutual reinforcement relationship between ranking and Web-snippet clustering does exist. In fact, the better the ranking of the underlying search engines, the more relevant the results from which SnakeT distills the hierarchy of labeled folders, and hence the more useful this hierarchy is to the user. Vice versa, the more intelligible the folder hierarchy, the more effective the personalization offered by SnakeT on the ranking of the query results. Copyright © 2007 John Wiley & Sons, Ltd. This work was done while the second author was a PhD student at the Dipartimento di Informatica, University of Pisa. The work contains the complete description and a full set of experiments on the software system SnakeT, which was partially published in the Proceedings of the 14th International World Wide Web Conference, Chiba, Japan, 2005

120 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