TY - GEN
T1 - Predicting POI visits with a heterogeneous information network
AU - Wang, Zih Syuan
AU - Juang, Jing Fu
AU - Teng, Wei Guang
PY - 2016/2/12
Y1 - 2016/2/12
N2 - A point of interest (POI) is a specific location that people may find useful or interesting. Examples include restaurants, stores, attractions, and hotels. With recent proliferation of location-based social networks (LBSNs), numerous users are gathered to share information on various POIs and to interact with each other. POI recommendation is then a crucial issue because it not only helps users to explore potential places but also gives LBSN providers a chance to post POI advertisements. As we utilize a heterogeneous information network to represent a LBSN in this work, POI recommendation is remodeled as a link prediction problem, which is significant in the field of social network analysis. Moreover, we propose to utilize the meta-path-based approach to extract implicit (but potentially useful) relationships between a user and a POI. Then, the extracted topological features are used to construct a prediction model with appropriate data classification techniques. In our experimental studies, the Yelp dataset is utilized as our testbed for performance evaluation purposes. Results of the experiments show that our prediction model is of good prediction quality in practical applications.
AB - A point of interest (POI) is a specific location that people may find useful or interesting. Examples include restaurants, stores, attractions, and hotels. With recent proliferation of location-based social networks (LBSNs), numerous users are gathered to share information on various POIs and to interact with each other. POI recommendation is then a crucial issue because it not only helps users to explore potential places but also gives LBSN providers a chance to post POI advertisements. As we utilize a heterogeneous information network to represent a LBSN in this work, POI recommendation is remodeled as a link prediction problem, which is significant in the field of social network analysis. Moreover, we propose to utilize the meta-path-based approach to extract implicit (but potentially useful) relationships between a user and a POI. Then, the extracted topological features are used to construct a prediction model with appropriate data classification techniques. In our experimental studies, the Yelp dataset is utilized as our testbed for performance evaluation purposes. Results of the experiments show that our prediction model is of good prediction quality in practical applications.
UR - http://www.scopus.com/inward/record.url?scp=84964308267&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964308267&partnerID=8YFLogxK
U2 - 10.1109/TAAI.2015.7407077
DO - 10.1109/TAAI.2015.7407077
M3 - Conference contribution
AN - SCOPUS:84964308267
T3 - TAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence
SP - 388
EP - 395
BT - TAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Conference on Technologies and Applications of Artificial Intelligence, TAAI 2015
Y2 - 20 November 2015 through 22 November 2015
ER -