Scalable collaborative filtering based on efficient identification of similar users

Sang Chul Lee; Si Yong Lee; Dong Kyu Chae; Sang Wook Kim

doi:10.1109/ICNIDC.2016.7974566

Scalable collaborative filtering based on efficient identification of similar users

Sang Chul Lee
, Si Yong Lee
, Dong Kyu Chae
, Sang Wook Kim

Division of Nanotechnology

Hanyang University

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

Abstract

User-based collaborative filtering suffers from significant amount of computational overhead to find users similar to a target user. To reduce the overhead, we propose a novel method to identify unnecessary users and items in computing the similarity. Also, we propose a data structure to support the method quite efficiently. Through extensive experiments, we show the proposed method outperforms traditional methods up to 33.8 times.

Original language	English
Title of host publication	Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	210-213
Number of pages	4
ISBN (Electronic)	9781509012459
DOIs	https://doi.org/10.1109/ICNIDC.2016.7974566
State	Published - 10 Jul 2017
Event	5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016 - Beijing, China Duration: 23 Sep 2016 → 25 Sep 2016

Publication series

Name	Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016

Conference

Conference	5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016
Country/Territory	China
City	Beijing
Period	23/09/16 → 25/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Collaborative filtering
Efficiency
Multidimensional indexing

Access to Document

10.1109/ICNIDC.2016.7974566

Cite this

Lee, S. C., Lee, S. Y., Chae, D. K., & Kim, S. W. (2017). Scalable collaborative filtering based on efficient identification of similar users. In Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016 (pp. 210-213). Article 7974566 (Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNIDC.2016.7974566

Lee, Sang Chul ; Lee, Si Yong ; Chae, Dong Kyu et al. / Scalable collaborative filtering based on efficient identification of similar users. Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 210-213 (Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016).

@inproceedings{1fd85c68c1554746bf8596e83f8d62b8,

title = "Scalable collaborative filtering based on efficient identification of similar users",

abstract = "User-based collaborative filtering suffers from significant amount of computational overhead to find users similar to a target user. To reduce the overhead, we propose a novel method to identify unnecessary users and items in computing the similarity. Also, we propose a data structure to support the method quite efficiently. Through extensive experiments, we show the proposed method outperforms traditional methods up to 33.8 times.",

keywords = "Collaborative filtering, Efficiency, Multidimensional indexing",

author = "Lee, \{Sang Chul\} and Lee, \{Si Yong\} and Chae, \{Dong Kyu\} and Kim, \{Sang Wook\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016 ; Conference date: 23-09-2016 Through 25-09-2016",

year = "2017",

month = jul,

day = "10",

doi = "10.1109/ICNIDC.2016.7974566",

language = "English",

series = "Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "210--213",

booktitle = "Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016",

}

Lee, SC, Lee, SY, Chae, DK & Kim, SW 2017, Scalable collaborative filtering based on efficient identification of similar users. in Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016., 7974566, Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 210-213, 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016, Beijing, China, 23/09/16. https://doi.org/10.1109/ICNIDC.2016.7974566

Scalable collaborative filtering based on efficient identification of similar users. / Lee, Sang Chul; Lee, Si Yong; Chae, Dong Kyu et al.
Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 210-213 7974566 (Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016).

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

TY - GEN

T1 - Scalable collaborative filtering based on efficient identification of similar users

AU - Lee, Sang Chul

AU - Lee, Si Yong

AU - Chae, Dong Kyu

AU - Kim, Sang Wook

PY - 2017/7/10

Y1 - 2017/7/10

N2 - User-based collaborative filtering suffers from significant amount of computational overhead to find users similar to a target user. To reduce the overhead, we propose a novel method to identify unnecessary users and items in computing the similarity. Also, we propose a data structure to support the method quite efficiently. Through extensive experiments, we show the proposed method outperforms traditional methods up to 33.8 times.

AB - User-based collaborative filtering suffers from significant amount of computational overhead to find users similar to a target user. To reduce the overhead, we propose a novel method to identify unnecessary users and items in computing the similarity. Also, we propose a data structure to support the method quite efficiently. Through extensive experiments, we show the proposed method outperforms traditional methods up to 33.8 times.

KW - Collaborative filtering

KW - Efficiency

KW - Multidimensional indexing

UR - https://www.scopus.com/pages/publications/85027519300

U2 - 10.1109/ICNIDC.2016.7974566

DO - 10.1109/ICNIDC.2016.7974566

M3 - Conference contribution

AN - SCOPUS:85027519300

T3 - Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016

SP - 210

EP - 213

BT - Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016

Y2 - 23 September 2016 through 25 September 2016

ER -

Lee SC, Lee SY, Chae DK, Kim SW. Scalable collaborative filtering based on efficient identification of similar users. In Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 210-213. 7974566. (Proceedings of 2016 5th International Conference on Network Infrastructure and Digital Content, IEEE IC-NIDC 2016). doi: 10.1109/ICNIDC.2016.7974566

Scalable collaborative filtering based on efficient identification of similar users

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this