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A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories

Published: 16 August 2017 Publication History

Abstract

Two characteristics of location-based services are mobile trajectories and the ability to facilitate social networking. The recording of trajectory data contributes valuable resources towards understanding users’ geographical movement behaviors. Social networking is possible when users are able to quickly connect to anyone nearby. A social network with location based services is known as location-based social network (LBSN). As shown in Cho et al. [2013], locations that are frequently visited by socially related persons tend to be correlated, which indicates the close association between social connections and trajectory behaviors of users in LBSNs. To better analyze and mine LBSN data, we need to have a comprehensive view of each of these two aspects, i.e., the mobile trajectory data and the social network.
Specifically, we present a novel neural network model that can jointly model both social networks and mobile trajectories. Our model consists of two components: the construction of social networks and the generation of mobile trajectories. First we adopt a network embedding method for the construction of social networks: a networking representation can be derived for a user. The key to our model lies in generating mobile trajectories. Second, we consider four factors that influence the generation process of mobile trajectories: user visit preference, influence of friends, short-term sequential contexts, and long-term sequential contexts. To characterize the last two contexts, we employ the RNN and GRU models to capture the sequential relatedness in mobile trajectories at the short or long term levels. Finally, the two components are tied by sharing the user network representations. Experimental results on two important applications demonstrate the effectiveness of our model. In particular, the improvement over baselines is more significant when either network structure or trajectory data is sparse.

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cover image ACM Transactions on Information Systems
ACM Transactions on Information Systems  Volume 35, Issue 4
Special issue: Search, Mining and their Applications on Mobile Devices
October 2017
461 pages
ISSN:1046-8188
EISSN:1558-2868
DOI:10.1145/3112649
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 August 2017
Accepted: 01 January 2017
Revised: 01 December 2016
Received: 01 June 2016
Published in TOIS Volume 35, Issue 4

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Author Tags

  1. Link prediction
  2. friend recommendation
  3. next-location recommendation
  4. recurrent neural network

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  • Research-article
  • Research
  • Refereed

Funding Sources

  • Beijing Natural Science Foundation
  • Major Project of the National Social Science Foundation of China
  • Tsinghua University Initiative Scientific Research Program
  • 973 Program
  • HTC Beijing Research
  • National Natural Science Foundation of China

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