Neural network collaborative filtering for group recommendation

Wei Zhang; Yue Bai; Jun Zheng; Jiaona Pang

doi:10.1007/978-3-030-04224-0_12

Neural network collaborative filtering for group recommendation

Wei Zhang
, Yue Bai
, Jun Zheng^*
, Jiaona Pang

^*Corresponding author for this work

School of Computer Science and Technology

East China Normal University

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

2 Scopus citations

Abstract

In the group recommender system, most of methods through aggregating individual preferences of each member in the group to group preference, which neglect the correlation among the members of the group. In this paper, group recommendation based on neural collaborative filtering (GNCF) and convolutional neural collaborative filtering (GCNCF) frameworks are proposed, which simulate the interaction between the members of the group and make recommendations directly for the group. GNCF and GCNCF frameworks predict group ratings by learning user-item interaction matrices. They project sparse vectors to dense vectors by utilizing the full connection layer, and improve the non-linear capability of the model by using the deep neural networks. Comparing with the traditional method, our method builds a new group recommendation model, and its effectiveness is well demonstrated through experiments.

Original language	English
Title of host publication	Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
Editors	Long Cheng, Andrew Chi Sing Leung, Seiichi Ozawa
Publisher	Springer Verlag
Pages	131-143
Number of pages	13
ISBN (Print)	9783030042233
DOIs	https://doi.org/10.1007/978-3-030-04224-0_12
State	Published - 2018
Event	25th International Conference on Neural Information Processing, ICONIP 2018 - Siem Reap, Cambodia Duration: 13 Dec 2018 → 16 Dec 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11306 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Neural Information Processing, ICONIP 2018
Country/Territory	Cambodia
City	Siem Reap
Period	13/12/18 → 16/12/18

Keywords

Collaborative filtering
Context-aware
Group recommendation
Neural network

Access to Document

10.1007/978-3-030-04224-0_12

Cite this

Zhang, W., Bai, Y., Zheng, J., & Pang, J. (2018). Neural network collaborative filtering for group recommendation. In L. Cheng, A. C. S. Leung, & S. Ozawa (Eds.), Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings (pp. 131-143). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11306 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04224-0_12

Zhang, Wei ; Bai, Yue ; Zheng, Jun et al. / Neural network collaborative filtering for group recommendation. Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. editor / Long Cheng ; Andrew Chi Sing Leung ; Seiichi Ozawa. Springer Verlag, 2018. pp. 131-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5ba8a935bfdc45718a5ad5bd1d4dda79,

title = "Neural network collaborative filtering for group recommendation",

abstract = "In the group recommender system, most of methods through aggregating individual preferences of each member in the group to group preference, which neglect the correlation among the members of the group. In this paper, group recommendation based on neural collaborative filtering (GNCF) and convolutional neural collaborative filtering (GCNCF) frameworks are proposed, which simulate the interaction between the members of the group and make recommendations directly for the group. GNCF and GCNCF frameworks predict group ratings by learning user-item interaction matrices. They project sparse vectors to dense vectors by utilizing the full connection layer, and improve the non-linear capability of the model by using the deep neural networks. Comparing with the traditional method, our method builds a new group recommendation model, and its effectiveness is well demonstrated through experiments.",

keywords = "Collaborative filtering, Context-aware, Group recommendation, Neural network",

author = "Wei Zhang and Yue Bai and Jun Zheng and Jiaona Pang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 25th International Conference on Neural Information Processing, ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",

year = "2018",

doi = "10.1007/978-3-030-04224-0\_12",

language = "英语",

isbn = "9783030042233",

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

publisher = "Springer Verlag",

pages = "131--143",

editor = "Long Cheng and Leung, \{Andrew Chi Sing\} and Seiichi Ozawa",

booktitle = "Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings",

address = "德国",

}

Zhang, W, Bai, Y, Zheng, J & Pang, J 2018, Neural network collaborative filtering for group recommendation. in L Cheng, ACS Leung & S Ozawa (eds), Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11306 LNCS, Springer Verlag, pp. 131-143, 25th International Conference on Neural Information Processing, ICONIP 2018, Siem Reap, Cambodia, 13/12/18. https://doi.org/10.1007/978-3-030-04224-0_12

Neural network collaborative filtering for group recommendation. / Zhang, Wei; Bai, Yue; Zheng, Jun et al.
Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. ed. / Long Cheng; Andrew Chi Sing Leung; Seiichi Ozawa. Springer Verlag, 2018. p. 131-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11306 LNCS).

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

TY - GEN

T1 - Neural network collaborative filtering for group recommendation

AU - Zhang, Wei

AU - Bai, Yue

AU - Zheng, Jun

AU - Pang, Jiaona

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - In the group recommender system, most of methods through aggregating individual preferences of each member in the group to group preference, which neglect the correlation among the members of the group. In this paper, group recommendation based on neural collaborative filtering (GNCF) and convolutional neural collaborative filtering (GCNCF) frameworks are proposed, which simulate the interaction between the members of the group and make recommendations directly for the group. GNCF and GCNCF frameworks predict group ratings by learning user-item interaction matrices. They project sparse vectors to dense vectors by utilizing the full connection layer, and improve the non-linear capability of the model by using the deep neural networks. Comparing with the traditional method, our method builds a new group recommendation model, and its effectiveness is well demonstrated through experiments.

AB - In the group recommender system, most of methods through aggregating individual preferences of each member in the group to group preference, which neglect the correlation among the members of the group. In this paper, group recommendation based on neural collaborative filtering (GNCF) and convolutional neural collaborative filtering (GCNCF) frameworks are proposed, which simulate the interaction between the members of the group and make recommendations directly for the group. GNCF and GCNCF frameworks predict group ratings by learning user-item interaction matrices. They project sparse vectors to dense vectors by utilizing the full connection layer, and improve the non-linear capability of the model by using the deep neural networks. Comparing with the traditional method, our method builds a new group recommendation model, and its effectiveness is well demonstrated through experiments.

KW - Collaborative filtering

KW - Context-aware

KW - Group recommendation

KW - Neural network

UR - https://www.scopus.com/pages/publications/85059043792

U2 - 10.1007/978-3-030-04224-0_12

DO - 10.1007/978-3-030-04224-0_12

M3 - 会议稿件

AN - SCOPUS:85059043792

SN - 9783030042233

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

SP - 131

EP - 143

BT - Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings

A2 - Cheng, Long

A2 - Leung, Andrew Chi Sing

A2 - Ozawa, Seiichi

PB - Springer Verlag

T2 - 25th International Conference on Neural Information Processing, ICONIP 2018

Y2 - 13 December 2018 through 16 December 2018

ER -

Zhang W, Bai Y, Zheng J, Pang J. Neural network collaborative filtering for group recommendation. In Cheng L, Leung ACS, Ozawa S, editors, Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. Springer Verlag. 2018. p. 131-143. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-04224-0_12

Neural network collaborative filtering for group recommendation

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