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"Towards higher relevance and serendipity in scholarly paper recommendation" by Kazunari Sugiyama and Min-Yen Kan with Martin Vesely as coordinator

Published: 19 February 2015 Publication History

Abstract

Finding relevant scholarly papers is an important task for researchers. Such a literature search involves identifying drawbacks in existing works and proposing new approaches that address them. However, the growing number of scientific published papers results in information overload even for simple searches, such that researchers have difficulty in finding papers relevant to their interests. Recommendation systems can help address this problem to find relevant papers efficiently. In this article, we summarize our work on scholarly paper recommendation from both relevance and serendipitous perspectives. Experimental results on a publicly-available scholarly paper recommendation dataset show that our proposed approaches provides promising recommendations for researchers, outperforming the state-of-the-art with statistical significance.

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  • (2025)Misinformation, Disinformation, and Generative AI: Implications for Perception and PolicyDigital Government: Research and Practice10.1145/36893726:1(1-15)Online publication date: 12-Feb-2025
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Published In

cover image ACM SIGWEB Newsletter
ACM SIGWEB Newsletter  Volume 2015, Issue Winter
Winter 2015
37 pages
ISSN:1931-1745
EISSN:1931-1435
DOI:10.1145/2719943
Issue’s Table of Contents
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 February 2015
Published in SIGWEB Volume 2015, Issue Winter

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Cited By

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  • (2025)Misinformation, Disinformation, and Generative AI: Implications for Perception and PolicyDigital Government: Research and Practice10.1145/36893726:1(1-15)Online publication date: 12-Feb-2025
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  • (2023)An anatomization of research paper recommender systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105641118:COnline publication date: 1-Feb-2023
  • (2022)Researching Serendipity in Digital Information EnvironmentsundefinedOnline publication date: 10-Mar-2022
  • (2021)CLAVER: An integrated framework of convolutional layer, bidirectional LSTM with attention mechanism based scholarly venue recommendationInformation Sciences10.1016/j.ins.2020.12.024559(212-235)Online publication date: Jun-2021
  • (2021)Serendipity in Recommender Systems: A Systematic Literature ReviewJournal of Computer Science and Technology10.1007/s11390-020-0135-936:2(375-396)Online publication date: 1-Apr-2021
  • (2019)Research Paper Recommender System with Serendipity Using Tweets vs. DiversificationDigital Libraries at the Crossroads of Digital Information for the Future10.1007/978-3-030-34058-2_7(63-70)Online publication date: 29-Oct-2019
  • (2018)Newly Published Scientific Papers Recommendation in Heterogeneous Information NetworksMobile Networks and Applications10.1007/s11036-018-1133-9Online publication date: 25-Sep-2018
  • (2018)Comprehensive Study on Usage of Multi Objectives in Recommender SystemsComputational Vision and Bio Inspired Computing10.1007/978-3-319-71767-8_5(55-67)Online publication date: 20-Feb-2018
  • (2017)Researching Serendipity in Digital Information EnvironmentsSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00790ED1V01Y201707ICR0599:6(i-91)Online publication date: 28-Sep-2017

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