Investigating deciding factors of product recommendation in social media

Jou Yu Chen; Ping Yu Hsu; Ming Shien Cheng; Hong Tsuen Lei; Shih Hsiang Huang; Yen Huei Ko; Chen Wan Huang

doi:10.1007/978-3-319-93818-9_23

Investigating deciding factors of product recommendation in social media

Jou Yu Chen, Ping Yu Hsu, Ming Shien Cheng, Hong Tsuen Lei, Shih Hsiang Huang, Yen Huei Ko, Chen Wan Huang

Department of Business Administration

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

Abstract

With the growing popularity of social media, the number of people using social media to communicate and interact with others has increased steadily. As a result, social commerce has become a new phenomenon. In the past, most of the product recommendations in microblogging only dealt with personal preferences and interests, and ignored other possible factors such as Crowd Interest, Popularity of Products, Reputation of Creators, Types of Preference and Recent. Nowadays, these variables used by Facebook to recommend posts to their users. Therefore, this research adapted those five aspects and analyzed their effectiveness to recommend products on social media. This study used the Plurk API to develop and implement recommended robots that recommend products at specific times of the day so that they can get product information and meet recommended tasks in the social circle. The empirical results showed that the Interest, Popularity and Type have significant impacts on recommendation effectiveness.

Original language	English
Title of host publication	Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings
Editors	Ying Tan, Qirong Tang, Yuhui Shi
Publisher	Springer Verlag
Pages	249-257
Number of pages	9
ISBN (Print)	9783319938172
DOIs	https://doi.org/10.1007/978-3-319-93818-9_23
State	Published - 2018
Event	9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China Duration: 17 Jun 2018 → 22 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10942 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Swarm Intelligence, ICSI 2018
Country/Territory	China
City	Shanghai
Period	17/06/18 → 22/06/18

Keywords

NewsFeed
Recommendation
Social media

Access to Document

10.1007/978-3-319-93818-9_23

Cite this

Chen, J. Y., Hsu, P. Y., Cheng, M. S., Lei, H. T., Huang, S. H., Ko, Y. H., & Huang, C. W. (2018). Investigating deciding factors of product recommendation in social media. In Y. Tan, Q. Tang, & Y. Shi (Eds.), Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings (pp. 249-257). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10942 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93818-9_23

Chen, Jou Yu ; Hsu, Ping Yu ; Cheng, Ming Shien et al. / Investigating deciding factors of product recommendation in social media. Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings. editor / Ying Tan ; Qirong Tang ; Yuhui Shi. Springer Verlag, 2018. pp. 249-257 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a00399d555264863ba98fce5577e935c,

title = "Investigating deciding factors of product recommendation in social media",

abstract = "With the growing popularity of social media, the number of people using social media to communicate and interact with others has increased steadily. As a result, social commerce has become a new phenomenon. In the past, most of the product recommendations in microblogging only dealt with personal preferences and interests, and ignored other possible factors such as Crowd Interest, Popularity of Products, Reputation of Creators, Types of Preference and Recent. Nowadays, these variables used by Facebook to recommend posts to their users. Therefore, this research adapted those five aspects and analyzed their effectiveness to recommend products on social media. This study used the Plurk API to develop and implement recommended robots that recommend products at specific times of the day so that they can get product information and meet recommended tasks in the social circle. The empirical results showed that the Interest, Popularity and Type have significant impacts on recommendation effectiveness.",

keywords = "NewsFeed, Recommendation, Social media",

author = "Chen, {Jou Yu} and Hsu, {Ping Yu} and Cheng, {Ming Shien} and Lei, {Hong Tsuen} and Huang, {Shih Hsiang} and Ko, {Yen Huei} and Huang, {Chen Wan}",

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Chen, JY, Hsu, PY, Cheng, MS, Lei, HT, Huang, SH, Ko, YH & Huang, CW 2018, Investigating deciding factors of product recommendation in social media. in Y Tan, Q Tang & Y Shi (eds), Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10942 LNCS, Springer Verlag, pp. 249-257, 9th International Conference on Swarm Intelligence, ICSI 2018, Shanghai, China, 17/06/18. https://doi.org/10.1007/978-3-319-93818-9_23

Investigating deciding factors of product recommendation in social media. / Chen, Jou Yu; Hsu, Ping Yu; Cheng, Ming Shien et al.

Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings. ed. / Ying Tan; Qirong Tang; Yuhui Shi. Springer Verlag, 2018. p. 249-257 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10942 LNCS).

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

TY - GEN

T1 - Investigating deciding factors of product recommendation in social media

AU - Chen, Jou Yu

AU - Hsu, Ping Yu

AU - Cheng, Ming Shien

AU - Lei, Hong Tsuen

AU - Huang, Shih Hsiang

AU - Ko, Yen Huei

AU - Huang, Chen Wan

PY - 2018

Y1 - 2018

N2 - With the growing popularity of social media, the number of people using social media to communicate and interact with others has increased steadily. As a result, social commerce has become a new phenomenon. In the past, most of the product recommendations in microblogging only dealt with personal preferences and interests, and ignored other possible factors such as Crowd Interest, Popularity of Products, Reputation of Creators, Types of Preference and Recent. Nowadays, these variables used by Facebook to recommend posts to their users. Therefore, this research adapted those five aspects and analyzed their effectiveness to recommend products on social media. This study used the Plurk API to develop and implement recommended robots that recommend products at specific times of the day so that they can get product information and meet recommended tasks in the social circle. The empirical results showed that the Interest, Popularity and Type have significant impacts on recommendation effectiveness.

AB - With the growing popularity of social media, the number of people using social media to communicate and interact with others has increased steadily. As a result, social commerce has become a new phenomenon. In the past, most of the product recommendations in microblogging only dealt with personal preferences and interests, and ignored other possible factors such as Crowd Interest, Popularity of Products, Reputation of Creators, Types of Preference and Recent. Nowadays, these variables used by Facebook to recommend posts to their users. Therefore, this research adapted those five aspects and analyzed their effectiveness to recommend products on social media. This study used the Plurk API to develop and implement recommended robots that recommend products at specific times of the day so that they can get product information and meet recommended tasks in the social circle. The empirical results showed that the Interest, Popularity and Type have significant impacts on recommendation effectiveness.

KW - NewsFeed

KW - Recommendation

KW - Social media

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DO - 10.1007/978-3-319-93818-9_23

M3 - 會議論文篇章

AN - SCOPUS:85049085433

SN - 9783319938172

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

SP - 249

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BT - Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings

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A2 - Tang, Qirong

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PB - Springer Verlag

Y2 - 17 June 2018 through 22 June 2018

ER -

Chen JY, Hsu PY, Cheng MS, Lei HT, Huang SH, Ko YH et al. Investigating deciding factors of product recommendation in social media. In Tan Y, Tang Q, Shi Y, editors, Advances in Swarm Intelligence - 9th International Conference, ICSI 2018, Proceedings. Springer Verlag. 2018. p. 249-257. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-93818-9_23

Investigating deciding factors of product recommendation in social media

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Keywords

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