Exploiting side information for recommendation

Qing Guo; Zhu Sun; Yin Leng Theng

doi:10.1007/978-3-030-19274-7_46

Exploiting side information for recommendation

Qing Guo^*, Zhu Sun, Yin Leng Theng

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

Recommender systems have become extremely essential tools to help resolve the information overload problem for users. However, traditional recommendation techniques suffer from critical issues such as data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed by exploiting the side information. This tutorial aims to provide a comprehensive analysis of how to exploit various kinds of side information for improving recommendation performance. Specifically, we present the usage of side information from two perspectives: the representation and methodology. By this tutorial, researchers of recommender system would gain an in-depth understanding of how side information can be utilized for better recommendation performance.

Original language	English
Title of host publication	Web Engineering - 19th International Conference, ICWE 2019, Proceedings
Editors	Flavius Frasincar, Maxim Bakaev, In-Young Ko
Publisher	Springer Verlag
Pages	569-573
Number of pages	5
ISBN (Print)	9783030192730
DOIs	https://doi.org/10.1007/978-3-030-19274-7_46
Publication status	Published - 2019
Externally published	Yes
Event	19th International Conference on Web Engineering, ICWE 2019 - Daejeon, Korea, Republic of Duration: Jun 11 2019 → Jun 14 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11496 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Web Engineering, ICWE 2019
Country/Territory	Korea, Republic of
City	Daejeon
Period	6/11/19 → 6/14/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-19274-7_46

Cite this

Guo, Q., Sun, Z., & Theng, Y. L. (2019). Exploiting side information for recommendation. In F. Frasincar, M. Bakaev, & I.-Y. Ko (Eds.), Web Engineering - 19th International Conference, ICWE 2019, Proceedings (pp. 569-573). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11496 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-19274-7_46

Guo, Qing ; Sun, Zhu ; Theng, Yin Leng. / Exploiting side information for recommendation. Web Engineering - 19th International Conference, ICWE 2019, Proceedings. editor / Flavius Frasincar ; Maxim Bakaev ; In-Young Ko. Springer Verlag, 2019. pp. 569-573 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Exploiting side information for recommendation",

abstract = "Recommender systems have become extremely essential tools to help resolve the information overload problem for users. However, traditional recommendation techniques suffer from critical issues such as data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed by exploiting the side information. This tutorial aims to provide a comprehensive analysis of how to exploit various kinds of side information for improving recommendation performance. Specifically, we present the usage of side information from two perspectives: the representation and methodology. By this tutorial, researchers of recommender system would gain an in-depth understanding of how side information can be utilized for better recommendation performance.",

author = "Qing Guo and Zhu Sun and Theng, {Yin Leng}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 19th International Conference on Web Engineering, ICWE 2019 ; Conference date: 11-06-2019 Through 14-06-2019",

year = "2019",

doi = "10.1007/978-3-030-19274-7_46",

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isbn = "9783030192730",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Guo, Q, Sun, Z & Theng, YL 2019, Exploiting side information for recommendation. in F Frasincar, M Bakaev & I-Y Ko (eds), Web Engineering - 19th International Conference, ICWE 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11496 LNCS, Springer Verlag, pp. 569-573, 19th International Conference on Web Engineering, ICWE 2019, Daejeon, Korea, Republic of, 6/11/19. https://doi.org/10.1007/978-3-030-19274-7_46

Exploiting side information for recommendation. / Guo, Qing; Sun, Zhu; Theng, Yin Leng.
Web Engineering - 19th International Conference, ICWE 2019, Proceedings. ed. / Flavius Frasincar; Maxim Bakaev; In-Young Ko. Springer Verlag, 2019. p. 569-573 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11496 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Exploiting side information for recommendation

AU - Guo, Qing

AU - Sun, Zhu

AU - Theng, Yin Leng

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Recommender systems have become extremely essential tools to help resolve the information overload problem for users. However, traditional recommendation techniques suffer from critical issues such as data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed by exploiting the side information. This tutorial aims to provide a comprehensive analysis of how to exploit various kinds of side information for improving recommendation performance. Specifically, we present the usage of side information from two perspectives: the representation and methodology. By this tutorial, researchers of recommender system would gain an in-depth understanding of how side information can be utilized for better recommendation performance.

AB - Recommender systems have become extremely essential tools to help resolve the information overload problem for users. However, traditional recommendation techniques suffer from critical issues such as data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed by exploiting the side information. This tutorial aims to provide a comprehensive analysis of how to exploit various kinds of side information for improving recommendation performance. Specifically, we present the usage of side information from two perspectives: the representation and methodology. By this tutorial, researchers of recommender system would gain an in-depth understanding of how side information can be utilized for better recommendation performance.

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SN - 9783030192730

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 569

EP - 573

BT - Web Engineering - 19th International Conference, ICWE 2019, Proceedings

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T2 - 19th International Conference on Web Engineering, ICWE 2019

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Guo Q, Sun Z, Theng YL. Exploiting side information for recommendation. In Frasincar F, Bakaev M, Ko IY, editors, Web Engineering - 19th International Conference, ICWE 2019, Proceedings. Springer Verlag. 2019. p. 569-573. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-19274-7_46

Exploiting side information for recommendation

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Access to Document

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Cite this