short-paper

Conceptual recommender system for CiteSeerX

Authors:

Ajith Kodakateri Pudhiyaveetil,

Susan Gauch,

Hiep Luong,

Josh EnoAuthors Info & Claims

RecSys '09: Proceedings of the third ACM conference on Recommender systems

Pages 241 - 244

https://doi.org/10.1145/1639714.1639758

Published: 23 October 2009 Publication History

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Abstract

Short search engine queries do not provide contextual information, making it difficult for traditional search engines to understand what users are really requesting. One approach to this problem is to use recommender systems that identify user interests through various methods in order to provide information specific to the user's needs. However, many current recommender systems use a collaborative model based on a network of users to provide the recommendations, leading to problems in environments where network relationships are sparse or unknown. Content-based recommenders can avoid the sparsity problem but they may be inefficient for large document collections. In this paper, we propose a concept-based recommender system that recommends papers to general users of the CiteSeerx digital library of Computer Science research publications. We also represent a novel way of classifying documents and creating user profiles based on the ACM (Association for Computer Machinery) classification tree. Based on these user profiles which are built using past click histories, relevant papers in the domain are recommended to users. Experiments with a set of users on the CiteSeerX database show that our concept-based method provides accurate recommendations even with limited user profile histories.

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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
https://doi.org/10.1109/TSMC.2021.3072426
Pajuelo-Holguera FGómez-Pulido JOrtega F(2020)Performance of Two Approaches of Embedded Recommender SystemsElectronics10.3390/electronics90405469:4(546)Online publication date: 25-Mar-2020
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Selmene SKodia Z(2020)Recommender System Based on User's Tweets Sentiment AnalysisProceedings of the 4th International Conference on E-Commerce, E-Business and E-Government10.1145/3409929.3414744(96-102)Online publication date: 17-Jun-2020
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Index Terms

Conceptual recommender system for CiteSeerX
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

RecSys '09: Proceedings of the third ACM conference on Recommender systems

October 2009

442 pages

ISBN:9781605584355

DOI:10.1145/1639714

General Chairs:
Lawrence Bergman
IBM Research, NY, USA
,
Alex Tuzhilin
New York University, NY, USA
,
Program Chairs:
Robin Burke
DePaul University, USA
,
Alexander Felfernig
Graz University of Technology, Austria
,
Lars Schmidt-Thieme
University of Hildesheim, Germany

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2009

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RecSys '09

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SIGCHI

RecSys '09: Third ACM Conference on Recommender Systems

October 23 - 25, 2009

New York, New York, USA

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

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Cited By

View all

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
Pajuelo-Holguera FGómez-Pulido JOrtega F(2020)Performance of Two Approaches of Embedded Recommender SystemsElectronics10.3390/electronics90405469:4(546)Online publication date: 25-Mar-2020
https://doi.org/10.3390/electronics9040546
Selmene SKodia Z(2020)Recommender System Based on User's Tweets Sentiment AnalysisProceedings of the 4th International Conference on E-Commerce, E-Business and E-Government10.1145/3409929.3414744(96-102)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1145/3409929.3414744
Patel KCaragea CPhillips MFox NHuang RWu DMarchionini GHe DCunningham SHansen P(2020)Identifying Documents In-Scope of a Collection from Web ArchivesProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 202010.1145/3383583.3398540(167-176)Online publication date: Aug-2020
https://doi.org/10.1145/3383583.3398540
Nogueira RJiang ZCho KLin J(2020)Navigation-based candidate expansion and pretrained language models for citation recommendationScientometrics10.1007/s11192-020-03718-9Online publication date: 10-Oct-2020
https://doi.org/10.1007/s11192-020-03718-9
Maake BOjo SZuva T(2019)A Survey on Data Mining Techniques in Research Paper Recommender SystemsResearch Data Access and Management in Modern Libraries10.4018/978-1-5225-8437-7.ch006(119-143)Online publication date: 2019
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Maake BOjo SZuva T(2019)Information Processing in Research Paper Recommender System ClassesResearch Data Access and Management in Modern Libraries10.4018/978-1-5225-8437-7.ch005(90-118)Online publication date: 2019
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Kanakia AShen ZEide DWang K(2019)A Scalable Hybrid Research Paper Recommender System for Microsoft AcademicThe World Wide Web Conference10.1145/3308558.3313700(2893-2899)Online publication date: 13-May-2019
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Hallo MEscobar MLujan-Mora S(2018)Automatic Extraction and Management of Open Access Bibliographic Information2018 IEEE World Engineering Education Conference (EDUNINE)10.1109/EDUNINE.2018.8450950(1-5)Online publication date: Mar-2018
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Shirude SKolhe S(2018)Classification of Library Resources in Recommender System Using Machine Learning TechniquesSocial Transformation – Digital Way10.1007/978-981-13-1343-1_54(661-673)Online publication date: 24-Aug-2018
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