Using multi-objective optimization to solve the long tail problem in recommender system

Jiaona Pang; Jun Guo; Wei Zhang

doi:10.1007/978-3-030-16142-2_24

Using multi-objective optimization to solve the long tail problem in recommender system

Jiaona Pang, Jun Guo, Wei Zhang

School of Computer Science and Technology

East China Normal University

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

14 Scopus citations

Abstract

An improved algorithm for recommender system is proposed in this paper where not only accuracy but also comprehensiveness of recommendation items is considered. We use a weighted similarity measure based on non-dominated sorting genetic algorithm II (NSGA-II). The solution of optimal weight vector is transformed into the multi-objective optimization problem. Both accuracy and coverage are taken as the objective functions simultaneously. Experimental results show that the proposed algorithm improves the coverage while the accuracy is kept.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
Editors	Qiang Yang, Zhiguo Gong, Min-Ling Zhang, Sheng-Jun Huang, Zhi-Hua Zhou
Publisher	Springer Verlag
Pages	302-313
Number of pages	12
ISBN (Print)	9783030161415
DOIs	https://doi.org/10.1007/978-3-030-16142-2_24
State	Published - 2019
Event	23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China Duration: 14 Apr 2019 → 17 Apr 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11441 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
Country/Territory	China
City	Macau
Period	14/04/19 → 17/04/19

Keywords

Multi-objective optimization
Recommender system
Weighted similarity measure

Access to Document

10.1007/978-3-030-16142-2_24

Cite this

Pang, J., Guo, J., & Zhang, W. (2019). Using multi-objective optimization to solve the long tail problem in recommender system. In Q. Yang, Z. Gong, M.-L. Zhang, S.-J. Huang, & Z.-H. Zhou (Eds.), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings (pp. 302-313). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11441 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-16142-2_24

Pang, Jiaona ; Guo, Jun ; Zhang, Wei. / Using multi-objective optimization to solve the long tail problem in recommender system. Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. editor / Qiang Yang ; Zhiguo Gong ; Min-Ling Zhang ; Sheng-Jun Huang ; Zhi-Hua Zhou. Springer Verlag, 2019. pp. 302-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "An improved algorithm for recommender system is proposed in this paper where not only accuracy but also comprehensiveness of recommendation items is considered. We use a weighted similarity measure based on non-dominated sorting genetic algorithm II (NSGA-II). The solution of optimal weight vector is transformed into the multi-objective optimization problem. Both accuracy and coverage are taken as the objective functions simultaneously. Experimental results show that the proposed algorithm improves the coverage while the accuracy is kept.",

keywords = "Multi-objective optimization, Recommender system, Weighted similarity measure",

author = "Jiaona Pang and Jun Guo and Wei Zhang",

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year = "2019",

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Pang, J, Guo, J & Zhang, W 2019, Using multi-objective optimization to solve the long tail problem in recommender system. in Q Yang, Z Gong, M-L Zhang, S-J Huang & Z-H Zhou (eds), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11441 LNAI, Springer Verlag, pp. 302-313, 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, Macau, China, 14/04/19. https://doi.org/10.1007/978-3-030-16142-2_24

Using multi-objective optimization to solve the long tail problem in recommender system. / Pang, Jiaona; Guo, Jun; Zhang, Wei.
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. ed. / Qiang Yang; Zhiguo Gong; Min-Ling Zhang; Sheng-Jun Huang; Zhi-Hua Zhou. Springer Verlag, 2019. p. 302-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11441 LNAI).

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

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

PY - 2019

Y1 - 2019

N2 - An improved algorithm for recommender system is proposed in this paper where not only accuracy but also comprehensiveness of recommendation items is considered. We use a weighted similarity measure based on non-dominated sorting genetic algorithm II (NSGA-II). The solution of optimal weight vector is transformed into the multi-objective optimization problem. Both accuracy and coverage are taken as the objective functions simultaneously. Experimental results show that the proposed algorithm improves the coverage while the accuracy is kept.

AB - An improved algorithm for recommender system is proposed in this paper where not only accuracy but also comprehensiveness of recommendation items is considered. We use a weighted similarity measure based on non-dominated sorting genetic algorithm II (NSGA-II). The solution of optimal weight vector is transformed into the multi-objective optimization problem. Both accuracy and coverage are taken as the objective functions simultaneously. Experimental results show that the proposed algorithm improves the coverage while the accuracy is kept.

KW - Multi-objective optimization

KW - Recommender system

KW - Weighted similarity measure

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A2 - Gong, Zhiguo

A2 - Zhang, Min-Ling

A2 - Huang, Sheng-Jun

A2 - Zhou, Zhi-Hua

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Pang J, Guo J, Zhang W. Using multi-objective optimization to solve the long tail problem in recommender system. In Yang Q, Gong Z, Zhang ML, Huang SJ, Zhou ZH, editors, Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. Springer Verlag. 2019. p. 302-313. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-16142-2_24

Using multi-objective optimization to solve the long tail problem in recommender system

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