Abstract
Geo-Social Networks (GSN) significantly improve location-aware capability of services by offering geo-located content based on the huge volumes of data generated in the GSN. The problem of user location prediction based on user-generated data in GSN has been extensively studied. However, existing studies are either concerning predicting users’ next check-in location or predicting their future check-in location at a given time with coarse granularity. A unified model that can predict both scenarios with fine granularity is quite rare. Also, due to the heterogeneity of multiple factors associated with both locations and users, how to efficiently incorporate these information still remains challenging. Inspired by the recent success of word embedding in natural language processing, in this paper, we propose a novel embedding model called Venue2Vec which automatically incorporates temporal-spatial context, semantic information, and sequential relations for fine-grained user location prediction. Locations of the same type, and those that are geographically close or often visited successively by users will be situated closer within the embedding space. Based on our proposed Venue2Vec model, we design techniques that allow for predicting a user’s next check-in location, and also their future check-in location at a given time. We conduct experiments on three real-world GSN datasets to verify the performance of the proposed model. Experimental results on both tasks show that Venue2Vec model outperforms several state-of-the-art models on various evaluation metrics. Furthermore, we show how the Venue2Vec model can be more time-efficient due to being parallelizable.
Highlights
With the popularity of intelligent mobile terminals and the progress of positioning technology, Geo-Social Networks (GSN), which can simultaneously provide locationbased service and online social networking service, have become increasingly prevalent
Inspired by the recent success of word embedding in natural language processing, in this work, we propose a novel embedding model called Venue2Vec which automatically incorporates temporal-spatial context, semantic information, and sequential relations for fine-grained user location prediction
Inspired by the recent success of word embedding in natural language processing and text mining, we propose a novel embedding model called Venue2Vec for fine-grained user location prediction in Geo-Social Networks
Summary
With the popularity of intelligent mobile terminals and the progress of positioning technology, Geo-Social Networks (GSN), which can simultaneously provide locationbased service and online social networking service, have become increasingly prevalent. Users are able to share check-in records with their friends, which bridges users’ online behavior with offline mobility. As it is rich in temporal, spatial and semantic information, the huge volume of user check-in data generated in GSN makes it possible to explore intrinsic pattern of user mobility. Such patterns could predict where a user would visit in the future based on his/her historical check-in records. From the individual point of view, accurate location prediction can provide users with informative personalized product recommendation [1, 2]. From a societal view, such analysis can accurately predict where traffic jams would happen, can be helpful for urban intelligent transportation [3, 4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
