Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network

Jian Zhong Wu; Chi Fu Hung; Chiu Ching Tuan

doi:10.3233/978-1-61499-484-8-1551

Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network

Jian Zhong Wu
, Chi Fu Hung
, Chiu Ching Tuan

Department of Electronic Engineering

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

Abstract

The people interest will often be changed by the dynamic of environment. We proposed the Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network (DTPCS), combining user dynamic similarity and contextual information. Through the Community Network, the friends of similar interests could be found by the check-in information. And give different weight value as the intimacy. We considered the changes of user requirements in different contexts, and explore the impact of contextual factors. The different contextual information will give different weight value to be as a basis for recommendation. Finally, the system sorts the recommend sequence to be a Top-N recommendation list. According to the simulation results, in recommend error, DTPCS is less than the others about 35 %. In recommend calculation time, DTPCS is less than the others about 32 %. In recommend coverage, DTPCS is more than the others about 27 %.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
Editors	William Cheng-Chung Chu, Han-Chieh Chao, Stephen Jenn-Hwa Yang
Publisher	IOS Press BV
Pages	1551-1560
Number of pages	10
ISBN (Electronic)	9781614994831
DOIs	https://doi.org/10.3233/978-1-61499-484-8-1551
State	Published - 2015
Event	International Computer Symposium, ICS 2014 - Taichung, Taiwan Duration: 12 Dec 2014 → 14 Dec 2014

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	274
ISSN (Print)	0922-6389
ISSN (Electronic)	1879-8314

Conference

Conference	International Computer Symposium, ICS 2014
Country/Territory	Taiwan
City	Taichung
Period	12/12/14 → 14/12/14

Bibliographical note

Publisher Copyright:
© 2015 The authors and IOS Press. All rights reserved.

Keywords

Collaborative Filtering
Community
Contextual Information
Recommendation System
Temporal Dynamics

Access to Document

10.3233/978-1-61499-484-8-1551

Cite this

Wu, J. Z., Hung, C. F., & Tuan, C. C. (2015). Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network. In W. C.-C. Chu, H.-C. Chao, & S. J.-H. Yang (Eds.), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014 (pp. 1551-1560). (Frontiers in Artificial Intelligence and Applications; Vol. 274). IOS Press BV. https://doi.org/10.3233/978-1-61499-484-8-1551

Wu, Jian Zhong ; Hung, Chi Fu ; Tuan, Chiu Ching. / Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network. Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. editor / William Cheng-Chung Chu ; Han-Chieh Chao ; Stephen Jenn-Hwa Yang. IOS Press BV, 2015. pp. 1551-1560 (Frontiers in Artificial Intelligence and Applications).

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title = "Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network",

abstract = "The people interest will often be changed by the dynamic of environment. We proposed the Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network (DTPCS), combining user dynamic similarity and contextual information. Through the Community Network, the friends of similar interests could be found by the check-in information. And give different weight value as the intimacy. We considered the changes of user requirements in different contexts, and explore the impact of contextual factors. The different contextual information will give different weight value to be as a basis for recommendation. Finally, the system sorts the recommend sequence to be a Top-N recommendation list. According to the simulation results, in recommend error, DTPCS is less than the others about 35 \%. In recommend calculation time, DTPCS is less than the others about 32 \%. In recommend coverage, DTPCS is more than the others about 27 \%.",

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Wu, JZ, Hung, CF & Tuan, CC 2015, Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network. in WC-C Chu, H-C Chao & SJ-H Yang (eds), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. Frontiers in Artificial Intelligence and Applications, vol. 274, IOS Press BV, pp. 1551-1560, International Computer Symposium, ICS 2014, Taichung, Taiwan, 12/12/14. https://doi.org/10.3233/978-1-61499-484-8-1551

Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network. / Wu, Jian Zhong; Hung, Chi Fu; Tuan, Chiu Ching.
Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. ed. / William Cheng-Chung Chu; Han-Chieh Chao; Stephen Jenn-Hwa Yang. IOS Press BV, 2015. p. 1551-1560 (Frontiers in Artificial Intelligence and Applications; Vol. 274).

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

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AB - The people interest will often be changed by the dynamic of environment. We proposed the Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network (DTPCS), combining user dynamic similarity and contextual information. Through the Community Network, the friends of similar interests could be found by the check-in information. And give different weight value as the intimacy. We considered the changes of user requirements in different contexts, and explore the impact of contextual factors. The different contextual information will give different weight value to be as a basis for recommendation. Finally, the system sorts the recommend sequence to be a Top-N recommendation list. According to the simulation results, in recommend error, DTPCS is less than the others about 35 %. In recommend calculation time, DTPCS is less than the others about 32 %. In recommend coverage, DTPCS is more than the others about 27 %.

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Wu JZ, Hung CF, Tuan CC. Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network. In Chu WCC, Chao HC, Yang SJH, editors, Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. IOS Press BV. 2015. p. 1551-1560. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/978-1-61499-484-8-1551

Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network

Abstract

Publication series

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Bibliographical note

Keywords

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