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Title:Explicit Graphical Relevance Feedback for Scholarly Information Retrieval
Author(s):Lee, Shaoshing; Guo, Chun; Liu, Xiaozhong
Subject(s):information seeking/retrieval
human-computer interaction
text/data/knowledge mining
Abstract:In this paper, we present a new method to collect users’ feedback on scientific heterogeneous graph to enhance the scientific information retrieval performance. Meanwhile, a new search system is implemented to validate the new feedback hypothesis. Unlike earlier approaches, by using the new search system scholars can mark the useful/not useful venues, papers, authors, and keywords on a heterogeneous graph, and the feedback algorithm can select the optimized paths on the graph to enhance the retrieval performance.
Issue Date:2015-03-15
Publisher:iSchools
Series/Report:iConference 2015 Proceedings
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/73725
Peer Reviewed:yes
Rights Information:Copyright 2015 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2015-03-24


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