TY - GEN
T1 - CoSoLoRec
T2 - 9th International Conference on Knowledge Science, Engineering and Management, KSEM 2016
AU - Guo, Hao
AU - Li, Xin
AU - He, Ming
AU - Zhao, Xiangyu
AU - Liu, Guiquan
AU - Xu, Guandong
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2016
Y1 - 2016
N2 - The pervasive use of Location-based Social Networks calls for more precise Point-of-Interest recommendation. The probability of a user’s visit to a target place is influenced by multiple factors. Though there are several fusion models in such fields, heterogeneous information are not considered comprehensively. To this end, we propose a novel probabilistic latent factor model by jointly considering the social correlation, geographical influence and users’ preference. To be specific, a variant of Latent Dirichlet Allocation is leveraged to extract the topics of both user and POI from reviews which is denoted as explicit interest. Then, Probabilistic Latent Factor Model is introduced to depict the implicit interest. Moreover, Kernel Density Estimation and friend-based Collaborative Filtering are leveraged to model user’s geographic allocation and social correlation respectively. Thus, we propose CoSoLoRec, a fusion framework, to ameliorate the recommendation. Experiments on two real-word datasets show the superiority of our approach over the state-of-the-art methods.
AB - The pervasive use of Location-based Social Networks calls for more precise Point-of-Interest recommendation. The probability of a user’s visit to a target place is influenced by multiple factors. Though there are several fusion models in such fields, heterogeneous information are not considered comprehensively. To this end, we propose a novel probabilistic latent factor model by jointly considering the social correlation, geographical influence and users’ preference. To be specific, a variant of Latent Dirichlet Allocation is leveraged to extract the topics of both user and POI from reviews which is denoted as explicit interest. Then, Probabilistic Latent Factor Model is introduced to depict the implicit interest. Moreover, Kernel Density Estimation and friend-based Collaborative Filtering are leveraged to model user’s geographic allocation and social correlation respectively. Thus, we propose CoSoLoRec, a fusion framework, to ameliorate the recommendation. Experiments on two real-word datasets show the superiority of our approach over the state-of-the-art methods.
KW - Heterogeneous information
KW - Location-based social network
KW - Point-of-Interest recommendation
KW - Probabilistic latent factor model
KW - Topic model
UR - http://www.scopus.com/inward/record.url?scp=84992616355&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84992616355&origin=recordpage
U2 - 10.1007/978-3-319-47650-6_48
DO - 10.1007/978-3-319-47650-6_48
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783319476490
VL - 9983 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 613
EP - 627
BT - Knowledge Science, Engineering and Management - 9th International Conference, KSEM 2016, Proceedings
PB - Springer Verlag
Y2 - 5 October 2016 through 7 October 2016
ER -