NEAR: Normalized network embedding with autoencoder for top-k item recommendation

Dedong Li; Aimin Zhou; Chuan Shi

doi:10.1007/978-3-030-16142-2_2

NEAR: Normalized network embedding with autoencoder for top-k item recommendation

Dedong Li
, Aimin Zhou^*
, Chuan Shi

^*Corresponding author for this work

School of Computer Science and Technology

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

Abstract

The recommendation system is an important tool both for business and individual users, aiming to generate a personalized recommended list for each user. Many studies have been devoted to improving the accuracy of recommendation, while have ignored the diversity of the results. We find that the key to addressing this problem is to fully exploit the hidden features of the heterogeneous user-item network, and consider the impact of hot items. Accordingly, we propose a personalized top-k item recommendation method that jointly considers accuracy and diversity, which is called Normalized Network Embedding with Autoencoder for Personalized Top-K Item Recommendation, namely NEAR. Our model fully exploits the hidden features of the heterogeneous user-item network data and generates more general low dimension embedding, resulting in more accurate and diverse recommendation sequences. We compare NEAR with some state-of-the-art algorithms on the DBLP and MovieLens1M datasets, and the experimental results show that our method is able to balance the accuracy and diversity scores.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
Editors	Sheng-Jun Huang, Min-Ling Zhang, Zhiguo Gong, Qiang Yang, Zhi-Hua Zhou
Publisher	Springer Verlag
Pages	15-26
Number of pages	12
ISBN (Print)	9783030161415
DOIs	https://doi.org/10.1007/978-3-030-16142-2_2
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

Autoencoder
Heterogeneous network
Network embedding
Recommendation system

Access to Document

10.1007/978-3-030-16142-2_2

Cite this

Li, D., Zhou, A., & Shi, C. (2019). NEAR: Normalized network embedding with autoencoder for top-k item recommendation. In S.-J. Huang, M.-L. Zhang, Z. Gong, Q. Yang, & Z.-H. Zhou (Eds.), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings (pp. 15-26). (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_2

Li, Dedong ; Zhou, Aimin ; Shi, Chuan. / NEAR : Normalized network embedding with autoencoder for top-k item recommendation. Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. editor / Sheng-Jun Huang ; Min-Ling Zhang ; Zhiguo Gong ; Qiang Yang ; Zhi-Hua Zhou. Springer Verlag, 2019. pp. 15-26 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0514dd64b97945e6a2222340b394f265,

title = "NEAR: Normalized network embedding with autoencoder for top-k item recommendation",

abstract = "The recommendation system is an important tool both for business and individual users, aiming to generate a personalized recommended list for each user. Many studies have been devoted to improving the accuracy of recommendation, while have ignored the diversity of the results. We find that the key to addressing this problem is to fully exploit the hidden features of the heterogeneous user-item network, and consider the impact of hot items. Accordingly, we propose a personalized top-k item recommendation method that jointly considers accuracy and diversity, which is called Normalized Network Embedding with Autoencoder for Personalized Top-K Item Recommendation, namely NEAR. Our model fully exploits the hidden features of the heterogeneous user-item network data and generates more general low dimension embedding, resulting in more accurate and diverse recommendation sequences. We compare NEAR with some state-of-the-art algorithms on the DBLP and MovieLens1M datasets, and the experimental results show that our method is able to balance the accuracy and diversity scores.",

keywords = "Autoencoder, Heterogeneous network, Network embedding, Recommendation system",

author = "Dedong Li and Aimin Zhou and Chuan Shi",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 ; Conference date: 14-04-2019 Through 17-04-2019",

year = "2019",

doi = "10.1007/978-3-030-16142-2\_2",

language = "英语",

isbn = "9783030161415",

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

publisher = "Springer Verlag",

pages = "15--26",

editor = "Sheng-Jun Huang and Min-Ling Zhang and Zhiguo Gong and Qiang Yang and Zhi-Hua Zhou",

booktitle = "Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings",

address = "德国",

}

Li, D, Zhou, A & Shi, C 2019, NEAR: Normalized network embedding with autoencoder for top-k item recommendation. in S-J Huang, M-L Zhang, Z Gong, Q Yang & 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. 15-26, 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_2

NEAR: Normalized network embedding with autoencoder for top-k item recommendation. / Li, Dedong; Zhou, Aimin; Shi, Chuan.
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. ed. / Sheng-Jun Huang; Min-Ling Zhang; Zhiguo Gong; Qiang Yang; Zhi-Hua Zhou. Springer Verlag, 2019. p. 15-26 (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

TY - GEN

T1 - NEAR

T2 - 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019

AU - Li, Dedong

AU - Zhou, Aimin

AU - Shi, Chuan

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

PY - 2019

Y1 - 2019

N2 - The recommendation system is an important tool both for business and individual users, aiming to generate a personalized recommended list for each user. Many studies have been devoted to improving the accuracy of recommendation, while have ignored the diversity of the results. We find that the key to addressing this problem is to fully exploit the hidden features of the heterogeneous user-item network, and consider the impact of hot items. Accordingly, we propose a personalized top-k item recommendation method that jointly considers accuracy and diversity, which is called Normalized Network Embedding with Autoencoder for Personalized Top-K Item Recommendation, namely NEAR. Our model fully exploits the hidden features of the heterogeneous user-item network data and generates more general low dimension embedding, resulting in more accurate and diverse recommendation sequences. We compare NEAR with some state-of-the-art algorithms on the DBLP and MovieLens1M datasets, and the experimental results show that our method is able to balance the accuracy and diversity scores.

AB - The recommendation system is an important tool both for business and individual users, aiming to generate a personalized recommended list for each user. Many studies have been devoted to improving the accuracy of recommendation, while have ignored the diversity of the results. We find that the key to addressing this problem is to fully exploit the hidden features of the heterogeneous user-item network, and consider the impact of hot items. Accordingly, we propose a personalized top-k item recommendation method that jointly considers accuracy and diversity, which is called Normalized Network Embedding with Autoencoder for Personalized Top-K Item Recommendation, namely NEAR. Our model fully exploits the hidden features of the heterogeneous user-item network data and generates more general low dimension embedding, resulting in more accurate and diverse recommendation sequences. We compare NEAR with some state-of-the-art algorithms on the DBLP and MovieLens1M datasets, and the experimental results show that our method is able to balance the accuracy and diversity scores.

KW - Autoencoder

KW - Heterogeneous network

KW - Network embedding

KW - Recommendation system

UR - https://www.scopus.com/pages/publications/85065046304

U2 - 10.1007/978-3-030-16142-2_2

DO - 10.1007/978-3-030-16142-2_2

M3 - 会议稿件

AN - SCOPUS:85065046304

SN - 9783030161415

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

SP - 15

EP - 26

BT - Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings

A2 - Huang, Sheng-Jun

A2 - Zhang, Min-Ling

A2 - Gong, Zhiguo

A2 - Yang, Qiang

A2 - Zhou, Zhi-Hua

PB - Springer Verlag

Y2 - 14 April 2019 through 17 April 2019

ER -

Li D, Zhou A, Shi C. NEAR: Normalized network embedding with autoencoder for top-k item recommendation. In Huang SJ, Zhang ML, Gong Z, Yang Q, Zhou ZH, editors, Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings. Springer Verlag. 2019. p. 15-26. (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_2

NEAR: Normalized network embedding with autoencoder for top-k item recommendation

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Keywords

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