TY - GEN
T1 - A fuzzy-based personalized recommender system for local businesses
AU - Tsai, Chun Hua
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/7/10
Y1 - 2016/7/10
N2 - On-line reviewing systems have become prevalent in our society. User-provided reviews of local businesses have provided rich information in terms of users' preferences regarding businesses and their interactions in reviewing systems; however, little is known about how the reviewing behaviors of users can benefit businesses in terms of suggesting potential collaboration opportunities. In the current study, we aim to build a recommendation system for businesses to provide suggestions for business collaboration. Based on historical data from Yelp that shows two businesses being reviewed by the same users within a same season, we were able to identify businesses that might attract the same customers in the future, and hence provide them with a collaboration suggestion. Our results suggest that the evidence-two businesses sharing reviews from same users-can provide recommendations for businesses to pursue future collaborative marketing opportunities.
AB - On-line reviewing systems have become prevalent in our society. User-provided reviews of local businesses have provided rich information in terms of users' preferences regarding businesses and their interactions in reviewing systems; however, little is known about how the reviewing behaviors of users can benefit businesses in terms of suggesting potential collaboration opportunities. In the current study, we aim to build a recommendation system for businesses to provide suggestions for business collaboration. Based on historical data from Yelp that shows two businesses being reviewed by the same users within a same season, we were able to identify businesses that might attract the same customers in the future, and hence provide them with a collaboration suggestion. Our results suggest that the evidence-two businesses sharing reviews from same users-can provide recommendations for businesses to pursue future collaborative marketing opportunities.
UR - http://www.scopus.com/inward/record.url?scp=84980392059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84980392059&partnerID=8YFLogxK
U2 - 10.1145/2914586.2914641
DO - 10.1145/2914586.2914641
M3 - Conference contribution
AN - SCOPUS:84980392059
T3 - HT 2016 - Proceedings of the 27th ACM Conference on Hypertext and Social Media
SP - 297
EP - 302
BT - HT 2016 - Proceedings of the 27th ACM Conference on Hypertext and Social Media
PB - Association for Computing Machinery, Inc
T2 - 27th ACM Conference on Hypertext and Social Media, HT 2016
Y2 - 10 July 2016 through 13 July 2016
ER -