A Fast, Interactive, Location-Based Food Recommendation Application

Sheng Min Chiu; Yi Chung Chen; Yow Shin Liou; Chiang Lee; Jia Ching Ying; Chee Hoe Loh; Jou Wei Lin

doi:10.1145/3504006.3504010

A Fast, Interactive, Location-Based Food Recommendation Application

Sheng Min Chiu, Yi Chung Chen, Yow Shin Liou, Chiang Lee, Jia Ching Ying, Chee Hoe Loh, Jou Wei Lin

Department of Computer Science and Information Engineering

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

Abstract

The public is increasingly using social media to facilitate the selection dining options. Most previous studies on this topic focused on the use of personal preferences to generate recommendations. Few studies considered (1) the common preferences of multiple users in a social network or (2) the geographic location of the establishments. In this study, we developed an interactive, location-based food recommendation application, which records the users' favorites, previous search history, and personal information. The proposed system is convenient to use and highly effective in identifying suitable candidate restaurants.

Original language	English
Title of host publication	Proceedings of the 8th Multidisciplinary International Social Networks Conference, MISNC 2021
Publisher	Association for Computing Machinery
Pages	21-25
Number of pages	5
ISBN (Electronic)	9781450396011
DOIs	https://doi.org/10.1145/3504006.3504010
Publication status	Published - 2022 Nov 15
Event	8th Multidisciplinary International Social Networks Conference, MISNC 2021 - Virtual, Online, Norway Duration: 2021 Nov 15 → 2021 Nov 17

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	8th Multidisciplinary International Social Networks Conference, MISNC 2021
Country/Territory	Norway
City	Virtual, Online
Period	21-11-15 → 21-11-17

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3504006.3504010

Cite this

Chiu, S. M., Chen, Y. C., Liou, Y. S., Lee, C., Ying, J. C., Loh, C. H., & Lin, J. W. (2022). A Fast, Interactive, Location-Based Food Recommendation Application. In Proceedings of the 8th Multidisciplinary International Social Networks Conference, MISNC 2021 (pp. 21-25). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3504006.3504010

@inproceedings{0a2c9e2115b14aee9c9fc00e00dcb4cc,

title = "A Fast, Interactive, Location-Based Food Recommendation Application",

abstract = "The public is increasingly using social media to facilitate the selection dining options. Most previous studies on this topic focused on the use of personal preferences to generate recommendations. Few studies considered (1) the common preferences of multiple users in a social network or (2) the geographic location of the establishments. In this study, we developed an interactive, location-based food recommendation application, which records the users' favorites, previous search history, and personal information. The proposed system is convenient to use and highly effective in identifying suitable candidate restaurants.",

author = "Chiu, {Sheng Min} and Chen, {Yi Chung} and Liou, {Yow Shin} and Chiang Lee and Ying, {Jia Ching} and Loh, {Chee Hoe} and Lin, {Jou Wei}",

note = "Funding Information: This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST 107-2119-M-224-003-MY3, MOST 110-2121-M-224-001, MOST 110-2221-E-006-176-, MOST 108-2621-M-006-007-and MOST 109-2221-E-005 -057-MY2. Publisher Copyright: {\textcopyright} 2021 ACM.; 8th Multidisciplinary International Social Networks Conference, MISNC 2021 ; Conference date: 15-11-2021 Through 17-11-2021",

year = "2022",

month = nov,

day = "15",

doi = "10.1145/3504006.3504010",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "21--25",

booktitle = "Proceedings of the 8th Multidisciplinary International Social Networks Conference, MISNC 2021",

}

Chiu, SM, Chen, YC, Liou, YS, Lee, C, Ying, JC, Loh, CH & Lin, JW 2022, A Fast, Interactive, Location-Based Food Recommendation Application. in Proceedings of the 8th Multidisciplinary International Social Networks Conference, MISNC 2021. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 21-25, 8th Multidisciplinary International Social Networks Conference, MISNC 2021, Virtual, Online, Norway, 21-11-15. https://doi.org/10.1145/3504006.3504010

A Fast, Interactive, Location-Based Food Recommendation Application. / Chiu, Sheng Min; Chen, Yi Chung; Liou, Yow Shin et al.
Proceedings of the 8th Multidisciplinary International Social Networks Conference, MISNC 2021. Association for Computing Machinery, 2022. p. 21-25 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - A Fast, Interactive, Location-Based Food Recommendation Application

AU - Chiu, Sheng Min

AU - Chen, Yi Chung

AU - Liou, Yow Shin

AU - Lee, Chiang

AU - Ying, Jia Ching

AU - Loh, Chee Hoe

AU - Lin, Jou Wei

N1 - Funding Information: This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST 107-2119-M-224-003-MY3, MOST 110-2121-M-224-001, MOST 110-2221-E-006-176-, MOST 108-2621-M-006-007-and MOST 109-2221-E-005 -057-MY2. Publisher Copyright: © 2021 ACM.

PY - 2022/11/15

Y1 - 2022/11/15

N2 - The public is increasingly using social media to facilitate the selection dining options. Most previous studies on this topic focused on the use of personal preferences to generate recommendations. Few studies considered (1) the common preferences of multiple users in a social network or (2) the geographic location of the establishments. In this study, we developed an interactive, location-based food recommendation application, which records the users' favorites, previous search history, and personal information. The proposed system is convenient to use and highly effective in identifying suitable candidate restaurants.

AB - The public is increasingly using social media to facilitate the selection dining options. Most previous studies on this topic focused on the use of personal preferences to generate recommendations. Few studies considered (1) the common preferences of multiple users in a social network or (2) the geographic location of the establishments. In this study, we developed an interactive, location-based food recommendation application, which records the users' favorites, previous search history, and personal information. The proposed system is convenient to use and highly effective in identifying suitable candidate restaurants.

UR - http://www.scopus.com/inward/record.url?scp=85145584992&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85145584992&partnerID=8YFLogxK

U2 - 10.1145/3504006.3504010

DO - 10.1145/3504006.3504010

M3 - Conference contribution

AN - SCOPUS:85145584992

T3 - ACM International Conference Proceeding Series

SP - 21

EP - 25

BT - Proceedings of the 8th Multidisciplinary International Social Networks Conference, MISNC 2021

PB - Association for Computing Machinery

T2 - 8th Multidisciplinary International Social Networks Conference, MISNC 2021

Y2 - 15 November 2021 through 17 November 2021

ER -

A Fast, Interactive, Location-Based Food Recommendation Application

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Cite this