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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
11 May 1998
TL;DR: It is indicated that a global index organization might outperform a local index organization in a tightly coupled environment.
Abstract: We consider a digital library distributed in a tightly coupled environment. The library is indexed by inverted files and the vector space model is used as ranking strategy. Using a simple analytical model coupled with a small simulator, we study how query performance is affected by the index organization, the network speed, and the disks transfer rate. Our results, which are based on the Tipster/Trec3 collection, indicate that a global index organization might outperform a local index organization.

104 citations

Journal ArticleDOI
TL;DR: It is argued that the ranking strategy should be context specific and a new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP) is proposed.
Abstract: The Internet and corporate intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. We argue that the ranking strategy should be context specific and we propose a , new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP). The new method was tested on TREC data and the results are very promising.

104 citations

Proceedings ArticleDOI
21 Aug 2011
TL;DR: This work identifies and considers two new biases in TCM that indicate that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer, and proposes a task-centric click model~(TCM), which characterizes user behavior related to a task as a collective whole.
Abstract: Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session.Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model~(TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.

104 citations

Proceedings ArticleDOI
02 Nov 2010
TL;DR: GeoSocialDB as mentioned in this paper is a location-aware query operator for location-based social networking services, namely, location based news feed, locationbased news ranking, and location based recommendation.
Abstract: Social networking applications have become very important web services that provide Internet-based platforms for their users to interact with their friends. With the advances in the location-aware hardware and software technologies, location-based social networking applications have been proposed to provide services for their users, taking into account both the spatial and social aspects. Unfortunately, none of existing location-based social networking applications is a holistic system nor equips database management systems to support scalable location-based social networking services. In this paper, we present GeoSocialDB; a holistic system providing three location-based social networking services, namely, location-based news feed, location-based news ranking, and location-based recommendation. In GeoSocialDB, we aim to implement these services as query operators inside a database engine to optimize the query processing performance. Within the GeoSocialDB framework, we discuss research challenges and directions towards the realization of scalable and practical query processing for location-based social networking services. In general, we discuss the challenges in designing location- and/or rank-aware query operators, materializing query answers, supporting continuous query processing, and providing privacy-aware query processing for our three location-based social networking services.

104 citations

Journal ArticleDOI
TL;DR: Providing semantic-distance-based query recommendations can help consumers with query formation during HIR, and no statistically significant impact on user satisfaction or the users' ability to accomplish the predefined retrieval task was found.

104 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