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

電子工程系

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

摘要

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 %.

原文	English
主出版物標題	Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
編輯	William Cheng-Chung Chu, Han-Chieh Chao, Stephen Jenn-Hwa Yang
發行者	IOS Press BV
頁面	1551-1560
頁數	10
ISBN（電子）	9781614994831
DOIs	https://doi.org/10.3233/978-1-61499-484-8-1551
出版狀態	Published - 2015
事件	International Computer Symposium, ICS 2014 - Taichung, Taiwan 持續時間: 12 12月 2014 → 14 12月 2014

出版系列

名字	Frontiers in Artificial Intelligence and Applications
卷	274
ISSN（列印）	0922-6389
ISSN（電子）	1879-8314

Conference

Conference	International Computer Symposium, ICS 2014
國家/地區	Taiwan
城市	Taichung
期間	12/12/14 → 14/12/14

文獻附註

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

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

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Link to publication in Scopus

引用此

Wu, J. Z., Hung, C. F., & Tuan, C. C. (2015). Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network. 於 W. C.-C. Chu, H.-C. Chao, & S. J.-H. Yang (編輯), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014 (頁 1551-1560). (Frontiers in Artificial Intelligence and Applications; 卷 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. 編輯 / William Cheng-Chung Chu ; Han-Chieh Chao ; Stephen Jenn-Hwa Yang. IOS Press BV, 2015. 頁 1551-1560 (Frontiers in Artificial Intelligence and Applications).

@inproceedings{5fe297866e8a4c8983b7cd47d2519202,

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 \%.",

keywords = "Collaborative Filtering, Community, Contextual Information, Recommendation System, Temporal Dynamics",

author = "Wu, \{Jian Zhong\} and Hung, \{Chi Fu\} and Tuan, \{Chiu Ching\}",

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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. 於 WC-C Chu, H-C Chao & SJ-H Yang (編輯), Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014. Frontiers in Artificial Intelligence and Applications, 卷 274, IOS Press BV, 頁 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. 編輯 / William Cheng-Chung Chu; Han-Chieh Chao; Stephen Jenn-Hwa Yang. IOS Press BV, 2015. p. 1551-1560 (Frontiers in Artificial Intelligence and Applications; 卷 274).

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

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T1 - Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network

AU - Wu, Jian Zhong

AU - Hung, Chi Fu

AU - Tuan, Chiu Ching

PY - 2015

Y1 - 2015

N2 - 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 %.

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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KW - Contextual Information

KW - Recommendation System

KW - Temporal Dynamics

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A2 - Chu, William Cheng-Chung

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ER -

Wu JZ, Hung CF, Tuan CC. Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network. 於 Chu WCC, Chao HC, Yang SJH, 編輯, 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

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