TY - GEN
T1 - POI recommendation
T2 - 9th ACM Conference on Recommender Systems, RecSys 2015
AU - Griesner, Jean Benoît
AU - Abdessalem, Talel
AU - Naacke, Hubert
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/9/16
Y1 - 2015/9/16
N2 - Providing personalized point-of-interest (POI) recommendation has become a major issue with the rapid emergence of location-based social networks (LBSNs). Unlike traditional recommendation approaches, the LBSNs application domain comes with significant geographical and temporal dimensions. Moreover most of traditional recommendation algorithms fail to cope with the specific challenges implied by these two dimensions. Fusing geographical and temporal inuences for better recommendation accuracy in LBSNs remains unexplored, as far as we know. We depict how matrix factorization can serve POI recommendation, and propose a novel attempt to integrate both geographical and temporal inuences into matrix factorization. Specifically we present GeoMF-TD, an extension of geographical matrix factorization with temporal dependencies. Our experiments on a real dataset shows up to 20% benefit on recommendation precision.
AB - Providing personalized point-of-interest (POI) recommendation has become a major issue with the rapid emergence of location-based social networks (LBSNs). Unlike traditional recommendation approaches, the LBSNs application domain comes with significant geographical and temporal dimensions. Moreover most of traditional recommendation algorithms fail to cope with the specific challenges implied by these two dimensions. Fusing geographical and temporal inuences for better recommendation accuracy in LBSNs remains unexplored, as far as we know. We depict how matrix factorization can serve POI recommendation, and propose a novel attempt to integrate both geographical and temporal inuences into matrix factorization. Specifically we present GeoMF-TD, an extension of geographical matrix factorization with temporal dependencies. Our experiments on a real dataset shows up to 20% benefit on recommendation precision.
U2 - 10.1145/2792838.2799679
DO - 10.1145/2792838.2799679
M3 - Conference contribution
AN - SCOPUS:84962821251
T3 - RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
SP - 301
EP - 304
BT - RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
PB - Association for Computing Machinery, Inc
Y2 - 16 September 2015 through 20 September 2015
ER -