research-article

Automatically building research reading lists

Authors:

Michael D. Ekstrand,

Praveen Kannan,

James A. Stemper,

John T. Butler,

Joseph A. Konstan,

John T. RiedlAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 159 - 166

https://doi.org/10.1145/1864708.1864740

Published: 26 September 2010 Publication History

Abstract

All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node's importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an offline analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists.

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https://dl.acm.org/doi/10.1145/3589345
Pham PLe HTam NTran Q(2022)A Graph-Based Topic Modeling Approach to Detection of Irrelevant CitationsVietnam Journal of Computer Science10.1142/S219688882250033610:02(197-216)Online publication date: 5-Oct-2022
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Wang GZhang XWang HChu YShao Z(2022)Group-Oriented Paper Recommendation With Probabilistic Matrix Factorization and Evidential Reasoning in Scientific Social NetworkIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2021.307242652:6(3757-3771)Online publication date: Jun-2022
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Index Terms

Automatically building research reading lists
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Digital libraries and archives

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Published In

cover image ACM Conferences

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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https://dl.acm.org/doi/10.1145/3589345
Pham PLe HTam NTran Q(2022)A Graph-Based Topic Modeling Approach to Detection of Irrelevant CitationsVietnam Journal of Computer Science10.1142/S219688882250033610:02(197-216)Online publication date: 5-Oct-2022
https://doi.org/10.1142/S2196888822500336
Wang GZhang XWang HChu YShao Z(2022)Group-Oriented Paper Recommendation With Probabilistic Matrix Factorization and Evidential Reasoning in Scientific Social NetworkIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2021.307242652:6(3757-3771)Online publication date: Jun-2022
https://doi.org/10.1109/TSMC.2021.3072426
Ding JXiang TOu ZZuo WZhao RLin CZheng YLiu B(2022)Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00322(3426-3438)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00322
Jha GGaur MRanjan PThakur H(2021)A survey on trustworthy model of recommender systemInternational Journal of System Assurance Engineering and Management10.1007/s13198-021-01085-z14:S3(789-806)Online publication date: 13-Mar-2021
https://doi.org/10.1007/s13198-021-01085-z
Noei EHayat TPerrie JÇolak RHao YVembu SLyons KMolyneux S(2021)A qualitative study of large-scale recommendation algorithms for biomedical knowledge basesInternational Journal on Digital Libraries10.1007/s00799-021-00300-322:2(197-215)Online publication date: 1-Jun-2021
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Lin SLee GPeng S(2021)Academic Article Recommendation by Considering the Research Field TrajectoryIntelligent Computing Paradigm and Cutting-edge Technologies10.1007/978-3-030-65407-8_39(447-454)Online publication date: 22-Apr-2021
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Khadka ACantador IFernandez M(2020)Capturing and Exploiting Citation Knowledge for Recommending Recently Published Papers2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)10.1109/WETICE49692.2020.00054(239-244)Online publication date: Sep-2020
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Ma SZhang CLiu X(2020)A review of citation recommendation: from textual content to enriched contextScientometrics10.1007/s11192-019-03336-0Online publication date: 3-Jan-2020
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Tao MYang XGu GLi B(2020)Paper Recommend Based on LDA and PageRankArtificial Intelligence and Security10.1007/978-981-15-8101-4_51(571-584)Online publication date: 13-Sep-2020
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