Tag Recommendation by Word-Level Tag Sequence Modeling

Xuewen Shi; Heyan Huang; Shuyang Zhao; Ping Jian; Yi Kun Tang

doi:10.1007/978-3-030-18590-9_58

Tag Recommendation by Word-Level Tag Sequence Modeling

Xuewen Shi, Heyan Huang, Shuyang Zhao, Ping Jian^*, Yi Kun Tang

^*Corresponding author for this work

School of Computer Science and Technology

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

5 Citations (Scopus)

Abstract

In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. The model inherits the advantages of LSTM-based encoder for sequential modeling and attention-based decoder with local positional encodings for learning relations globally. Experimental results on Zhihu datasets illustrate the proposed model outperforms other state-of-the-art text classification based methods.

Original language	English
Title of host publication	Database Systems for Advanced Applications - DASFAA 2019 International Workshops
Subtitle of host publication	BDMS, BDQM, and GDMA, Proceedings
Editors	Guoliang Li, Joao Gama, Yongxin Tong, Juggapong Natwichai, Jun Yang
Publisher	Springer Verlag
Pages	420-424
Number of pages	5
ISBN (Print)	9783030185893
DOIs	https://doi.org/10.1007/978-3-030-18590-9_58
Publication status	Published - 2019
Event	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand Duration: 22 Apr 2019 → 25 Apr 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11448 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Country/Territory	Thailand
City	Chiang Mai
Period	22/04/19 → 25/04/19

Keywords

Multi-label classification
Tag generation
Tag recommendation

Access to Document

10.1007/978-3-030-18590-9_58

Cite this

Shi, X., Huang, H., Zhao, S., Jian, P., & Tang, Y. K. (2019). Tag Recommendation by Word-Level Tag Sequence Modeling. In G. Li, J. Gama, Y. Tong, J. Natwichai, & J. Yang (Eds.), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings (pp. 420-424). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11448 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_58

Shi, Xuewen ; Huang, Heyan ; Zhao, Shuyang et al. / Tag Recommendation by Word-Level Tag Sequence Modeling. Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. editor / Guoliang Li ; Joao Gama ; Yongxin Tong ; Juggapong Natwichai ; Jun Yang. Springer Verlag, 2019. pp. 420-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{363fa9a4bca44cf7837990d9c0699042,

title = "Tag Recommendation by Word-Level Tag Sequence Modeling",

abstract = "In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. The model inherits the advantages of LSTM-based encoder for sequential modeling and attention-based decoder with local positional encodings for learning relations globally. Experimental results on Zhihu datasets illustrate the proposed model outperforms other state-of-the-art text classification based methods.",

keywords = "Multi-label classification, Tag generation, Tag recommendation",

author = "Xuewen Shi and Heyan Huang and Shuyang Zhao and Ping Jian and Tang, {Yi Kun}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 ; Conference date: 22-04-2019 Through 25-04-2019",

year = "2019",

doi = "10.1007/978-3-030-18590-9_58",

language = "English",

isbn = "9783030185893",

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

publisher = "Springer Verlag",

pages = "420--424",

editor = "Guoliang Li and Joao Gama and Yongxin Tong and Juggapong Natwichai and Jun Yang",

booktitle = "Database Systems for Advanced Applications - DASFAA 2019 International Workshops",

address = "Germany",

}

Shi, X, Huang, H, Zhao, S, Jian, P & Tang, YK 2019, Tag Recommendation by Word-Level Tag Sequence Modeling. in G Li, J Gama, Y Tong, J Natwichai & J Yang (eds), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11448 LNCS, Springer Verlag, pp. 420-424, 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22/04/19. https://doi.org/10.1007/978-3-030-18590-9_58

Tag Recommendation by Word-Level Tag Sequence Modeling. / Shi, Xuewen; Huang, Heyan; Zhao, Shuyang et al.
Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. ed. / Guoliang Li; Joao Gama; Yongxin Tong; Juggapong Natwichai; Jun Yang. Springer Verlag, 2019. p. 420-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11448 LNCS).

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

TY - GEN

T1 - Tag Recommendation by Word-Level Tag Sequence Modeling

AU - Shi, Xuewen

AU - Huang, Heyan

AU - Zhao, Shuyang

AU - Jian, Ping

AU - Tang, Yi Kun

PY - 2019

Y1 - 2019

N2 - In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. The model inherits the advantages of LSTM-based encoder for sequential modeling and attention-based decoder with local positional encodings for learning relations globally. Experimental results on Zhihu datasets illustrate the proposed model outperforms other state-of-the-art text classification based methods.

AB - In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. The model inherits the advantages of LSTM-based encoder for sequential modeling and attention-based decoder with local positional encodings for learning relations globally. Experimental results on Zhihu datasets illustrate the proposed model outperforms other state-of-the-art text classification based methods.

KW - Multi-label classification

KW - Tag generation

KW - Tag recommendation

UR - http://www.scopus.com/inward/record.url?scp=85065436079&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-18590-9_58

DO - 10.1007/978-3-030-18590-9_58

M3 - Conference contribution

AN - SCOPUS:85065436079

SN - 9783030185893

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

SP - 420

EP - 424

BT - Database Systems for Advanced Applications - DASFAA 2019 International Workshops

A2 - Li, Guoliang

A2 - Gama, Joao

A2 - Tong, Yongxin

A2 - Natwichai, Juggapong

A2 - Yang, Jun

PB - Springer Verlag

T2 - 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019

Y2 - 22 April 2019 through 25 April 2019

ER -

Shi X, Huang H, Zhao S, Jian P, Tang YK. Tag Recommendation by Word-Level Tag Sequence Modeling. In Li G, Gama J, Tong Y, Natwichai J, Yang J, editors, Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Springer Verlag. 2019. p. 420-424. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-18590-9_58

Tag Recommendation by Word-Level Tag Sequence Modeling

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