research-article

Learning multiple graphs for document recommendations

Authors:

Kai Yu,

C. Lee GilesAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 141 - 150

https://doi.org/10.1145/1367497.1367517

Published: 21 April 2008 Publication History

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Abstract

The Web offers rich relational data with different semantics. In this paper, we address the problem of document recommendation in a digital library, where the documents in question are networked by citations and are associated with other entities by various relations. Due to the sparsity of a single graph and noise in graph construction, we propose a new method for combining multiple graphs to measure document similarities, where different factorization strategies are used based on the nature of different graphs. In particular, the new method seeks a single low-dimensional embedding of documents that captures their relative similarities in a latent space. Based on the obtained embedding, a new recommendation framework is developed using semi-supervised learning on graphs. In addition, we address the scalability issue and propose an incremental algorithm. The new incremental method significantly improves the efficiency by calculating the embedding for new incoming documents only. The new batch and incremental methods are evaluated on two real world datasets prepared from CiteSeer. Experiments demonstrate significant quality improvement for our batch method and significant efficiency improvement with tolerable quality loss for our incremental method.

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Cited By

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Bian WLiu SZhou YChen DLiao YFan ZWang A(2024)IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language ModelKnowledge Science, Engineering and Management10.1007/978-981-97-5489-2_24(270-282)Online publication date: 27-Jul-2024
https://doi.org/10.1007/978-981-97-5489-2_24
Bahrani PMinaei Bidgoli BParvin HMirzarzaei MKeshavarz A(2023)A Content-Collaborative Recommender System based on clustering and ontologySignal and Data Processing10.61186/jsdp.20.3.19720:3(197-224)Online publication date: 1-Dec-2023
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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
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Index Terms

Learning multiple graphs for document recommendations

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Reviews

Reviewer: Amos O Olagunju

The measurement of document similarities by using citations in a digital library naturally leads to fundamental questions. How should different graphs for measurement of document similarities in information retrieval systems be presented__?__ How should document citation (DC) graphs and bipartite graphs of the document-author (DA) and document-publication (DP) venue be explored to develop novel document similarity metrics__?__ Principles and applications of collaborative item filtering (CIF) in document similarity measurement exist in the literature [1,2,3]. However, the usefulness of CIF in networked document items (DIs) on the Web remains underexploited. The authors propose a new document recommendation framework predicated on the semi-supervised learning of multiple graphs. The framework includes a multiple graph learning model for accurately capturing the relative similarities among single low-dimensional embedded DIs in a latent semantic space, and a label propagation scheme for making use of the similarities among partly tagged documents to calculate approximately the labels of untagged documents. The authors combine the learning represented in the DC, DA, and DP graphs into an optimization problem, with factorization strategies to obtain the distinctive traits of the graphs. They present an incremental document embedding update algorithm for enhancing the scalability and efficiency of processing large documents in digital libraries. The standard conjugate gradient method is used to solve the optimization problems in both the learning and the incremental updating of document insertion. The proposed batch and incremental techniques for merging multiple graphs by document similarity measurement are evaluated using samples of datasets from real-world digital databases containing documents with citations, authors, and venues. Experimental precision and recall results of document recommendations show major quality enhancement with the batch technique, and a significant upgrade of efficiency, with acceptable quality depreciation, with the incremental method. Consequently, the planned graph-synthesizing methods are excellent candidates for document recommendation over cooperating networks of digital libraries. Online Computing Reviews Service

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Published In

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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WWW '08

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Bian WLiu SZhou YChen DLiao YFan ZWang A(2024)IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language ModelKnowledge Science, Engineering and Management10.1007/978-981-97-5489-2_24(270-282)Online publication date: 27-Jul-2024
https://doi.org/10.1007/978-981-97-5489-2_24
Bahrani PMinaei Bidgoli BParvin HMirzarzaei MKeshavarz A(2023)A Content-Collaborative Recommender System based on clustering and ontologySignal and Data Processing10.61186/jsdp.20.3.19720:3(197-224)Online publication date: 1-Dec-2023
https://doi.org/10.61186/jsdp.20.3.197
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
Zhang JZhu L(2022)Citation recommendation using semantic representation of cited papers’ relations and contentExpert Systems with Applications10.1016/j.eswa.2021.115826187(115826)Online publication date: Jan-2022
https://doi.org/10.1016/j.eswa.2021.115826
Pornprasit CLiu XKiattipadungkul PKertkeidkachorn NKim KNoraset THassan STuarob S(2022)Enhancing citation recommendation using citation network embeddingScientometrics10.1007/s11192-021-04196-3Online publication date: 11-Jan-2022
https://doi.org/10.1007/s11192-021-04196-3
McMahan PMcFarland D(2021)Creative Destruction: The Structural Consequences of Scientific CurationAmerican Sociological Review10.1177/000312242199632386:2(341-376)Online publication date: 18-Mar-2021
https://doi.org/10.1177/0003122421996323
Xie QZhu YHuang JDu PNie J(2021)Graph Neural Collaborative Topic Model for Citation RecommendationACM Transactions on Information Systems10.1145/347397340:3(1-30)Online publication date: 17-Nov-2021
https://dl.acm.org/doi/10.1145/3473973
Fan HWu KParvin HBeigi APho K(2021)A Hybrid Recommender System Using KNN and ClusteringInternational Journal of Information Technology & Decision Making10.1142/S021962202150005X20:02(553-596)Online publication date: 31-Mar-2021
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Kong XMao MWang WLiu JXu B(2021)VOPRec: Vector Representation Learning of Papers with Text Information and Structural Identity for RecommendationIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2018.28306989:1(226-237)Online publication date: 1-Jan-2021
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Kajla NMissen MLuqman MCoustaty MMehmood AChoi G(2020)Additive Angular Margin Loss in Deep Graph Neural Network Classifier for Learning Graph Edit DistanceIEEE Access10.1109/ACCESS.2020.30358868(201752-201761)Online publication date: 2020
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Recommendations

Naïve filterbots for robust cold-start recommendations

Integrating collaborative filtering and matching-based search for product recommendations

Recommendation algorithm based on improved spectral clustering and transfer learning

Reviews

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