Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Distance matters: geo-social metrics for online social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
Recommending Social Events from Mobile Phone Location Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
CLR: a collaborative location recommendation framework based on co-clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
An object based conceptual framework for location based social networking
Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
| Hi-index | 0.00 |
Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking services, such as GPS to allow users to "check in" at various locations and record their experience. In particular, users submit ratings or personal comments for their location/activity. The vast amount of data that is being generated by users with GPS devices, such as mobile phones, needs efficient methods for its effective management. In this paper, we have implemented an online prototype system, called Geo-social recommender system, where users can get recommendations on friends, locations and activities. For the friend recommendation task, we apply the FriendLink algorithm, which performs a local path traversal on the friendship network. In order to provide location/activity recommendations, we represent data by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. As more data is accumulated to the system, we use incremental solutions to update our tensor. We perform an experimental evaluation of our method with two real data sets and measure its effectiveness through recall/precision.