Retrieval Status Values in Information Retrieval Evaluation

2005 ◽  
pp. 224-227 ◽  
Author(s):  
Amélie Imafouo ◽  
Xavier Tannier
Keyword(s):  
Information Retrieval ◽  
Information Retrieval Evaluation

Report on the CIKM workshop on living labs for information retrieval evaluation

2014 ◽  
Vol 48 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Krisztian Balog ◽  
David Elsweiler ◽  
Evangelos Kanoulas ◽  
Liadh Kelly ◽  
Mark D. Smucker
Keyword(s):  
Information Retrieval ◽  
Information Retrieval Evaluation ◽  
Living Labs

scholarly journals Using Replicates in Information Retrieval Evaluation

2017 ◽  
Vol 36 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Ellen M. Voorhees ◽  
Daniel Samarov ◽  
Ian Soboroff
Keyword(s):  
Information Retrieval ◽  
Information Retrieval Evaluation

FGFIREM: A feature generation framework based on information retrieval evaluation measures

2019 ◽  
Vol 133 ◽  
pp. 75-85
Author(s):  
Yuan Lin ◽  
Bo Xu ◽  
Hongfei Lin ◽  
Kan Xu ◽  
Ping Zhang
Keyword(s):  
Information Retrieval ◽  
Feature Generation ◽  
Evaluation Measures ◽  
Information Retrieval Evaluation

Exploring multiple diversification strategies for academic citation contexts recommendation

2020 ◽  
Vol 38 (4) ◽  
pp. 821-842
Author(s):  
Haihua Chen ◽  
Yunhan Yang ◽  
Wei Lu ◽  
Jiangping Chen
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Semantic Distance ◽  
Evaluation Framework ◽  
Data Set ◽  
Content Type ◽  
User Interests ◽  
Information Retrieval Evaluation ◽  
Diversification Strategies ◽  
Citation Contexts

Purpose Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing. Design/methodology/approach This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeerX data set and then evaluates the generated citation context lists by applying a user case study on 30 articles. Findings Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeerX using a list sorted by citation counts. Practical implications This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization. Originality/value The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

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