Unbiased Low-Variance Estimators for Precision and Related Information Retrieval Effectiveness Measures

Author(s):  
Gordon V. Cormack ◽  
Maura R. Grossman
2011 ◽  
Vol 51 (4) ◽  
pp. 732-744 ◽  
Author(s):  
Nicole Lang Beebe ◽  
Jan Guynes Clark ◽  
Glenn B. Dietrich ◽  
Myung S. Ko ◽  
Daijin Ko

2021 ◽  
Author(s):  
Yijing Chen ◽  
Hanming Lin ◽  
Jin Zhang ◽  
Yiming Zhao

BACKGROUND Online health information retrieval has been a top choice for acquiring health information and knowledge by millions worldwide. OBJECTIVE This study aims to investigate consumers’ modification of retrieval platform switch paths across health-related search tasks and learning via such a change. METHODS A lab user experiment was designed to obtain data on consumers’ health information search behavior. Participants accomplished health-related information search tasks. Screen movements were recorded by EV screen-recording software. The participants underwent in-depth interviews immediately after finishing the tasks. Screen recordings and interview data were both coded and analyzed. RESULTS Three types of learning, including the similar transfer learning, optimizing learning, and SERP-guided learning were identified based on five change patterns of retrieval platform switch paths adopted by health information consumers from task 1 to task 2. Health information consumers’ retrieval platform switch based on information usefulness evaluation. And they accessed different amounts and types of health knowledge from different retrieval platforms. CONCLUSIONS The results suggest that health information consumers exhibit learning both through retrieval platform switching and the knowledge they consume during the search process. This facilitates the assessment of a certain retrieval platform’s usefulness by measuring the amount and types of health knowledge in each search result. This study also contributes to the enhancement of consumers’ health information retrieval abilities, and to helping optimize health information retrieval platforms by increasing their exposure to consumers and increasing the matching degree between knowledge types and consumer needs.


2013 ◽  
Vol 712-715 ◽  
pp. 2706-2711
Author(s):  
Xiao Qing Yu ◽  
Wen Gen Wang ◽  
Jian Hua Shi ◽  
Yun Hui Wang

Information retrieval is the activity to organize information in a certain way, and according to the users demand to find out the related information from a collection of resources. Retrieval process and technology can be based on metadata or full-text indexing. Most of the relevant information retrieval systems are devised on the computer. However, with the highly development of the embedded technology, some popular application have been developed on the platform. In this paper, we will introduce an information retrieval system on the iOS platform which is more convenient, practical, and effective compared with the traditional system. And we will introduce an application based on this system design. The experiments shown that this system was exactly effective utilized to retrieval audio information.


Author(s):  
TANVEER J. SIDDIQUI ◽  
UMA SHANKER TIWARY

Our research focuses on the use of local context through relation matching to improve retrieval effectiveness. An information retrieval (IR) model that integrates relation and keyword matching has been used in this work. The model takes advantage of any existing relational similarity between documents and query to improve retrieval effectiveness. It gives high rank to a document in which the query concepts are involved in similar relationships as in the query, as compared to those in which they are related differently. A conceptual graph (CG) representation has been used to capture relationship between concepts. A simplified form of graph matching has been used to keep our model computationally tractable. Structural variations have been captured during matching through simple heuristics. Four different CG similarity measures have been proposed and used to evaluate performance of our model. We observed a maximum improvement of 7.37% in precision with the second CG similarity measure. The document collection used in this study is CACM-3204. CG similarity measure proposed by us is simple, flexible and scalable and can find application in many IR related tasks like information filtering, information extraction, question answering, document summarization, etc.


2016 ◽  
Vol 42 (6) ◽  
pp. 725-747 ◽  
Author(s):  
Bilel Moulahi ◽  
Lynda Tamine ◽  
Sadok Ben Yahia

With the advent of Web search and the large amount of data published on the Web sphere, a tremendous amount of documents become strongly time-dependent. In this respect, the time dimension has been extensively exploited as a highly important relevance criterion to improve the retrieval effectiveness of document ranking models. Thus, a compelling research interest is going on the temporal information retrieval realm, which gives rise to several temporal search applications. In this article, we intend to provide a scrutinizing overview of time-aware information retrieval models. We specifically put the focus on the use of timeliness and its impact on the global value of relevance as well as on the retrieval effectiveness. First, we attempt to motivate the importance of temporal signals, whenever combined with other relevance features, in accounting for document relevance. Then, we review the relevant studies standing at the crossroads of both information retrieval and time according to three common information retrieval aspects: the query level, the document content level and the document ranking model level. We organize the related temporal-based approaches around specific information retrieval tasks and regarding the task at hand, we emphasize the importance of results presentation and particularly timelines to the end user. We also report a set of relevant research trends and avenues that can be explored in the future.


2013 ◽  
Vol 756-759 ◽  
pp. 1249-1253 ◽  
Author(s):  
Jin Cui Kang ◽  
Jing Long Gao

The agricultural information on the internet become more and more, it is very difficult to search accurate related information from such different information, in order to improve the efficiency of information retrieval on the internet, the intelligent searching technology of agricultural information based on ontology is proposed. The paper firstly introduces research on the agricultural ontology and information retrieval, and takes agriculture domain knowledge as research object, analyzes the characters of agricultural domain knowledge and semantics retrieval, then uses the agricultural ontology to make the structure of agriculture ontology knowledge, and constructs the related agricultural knowledge ontology and knowledge base, implementing the intelligent searching of the agricultural information. The results indicate that the application of agricultural ontology technology in the agricultural information retrieval not only achieves the intelligent retrieval of agricultural information, but also greatly improves the accuracy and reliability of agricultural information retrieval.


2019 ◽  
Vol 119 (2) ◽  
pp. 987-1008 ◽  
Author(s):  
Maryam Yaghtin ◽  
Hajar Sotudeh ◽  
Mahdieh Mirzabeigi ◽  
Seyed Mostafa Fakhrahmad ◽  
Mehdi Mohammadi

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