Cross-Urban Point-of-Interest Recommendation for Non-Natives

Cross-Urban Point-of-Interest Recommendation for Non-Natives

Tao Xu (Wuhan University, Wuhan, China), Yutao Ma (Wuhan University, Wuhan, China & WISET Automation Co., Ltd., Wuhan, China) and Qian Wang (Wuhan University, Wuhan, China)
Copyright: © 2018 |Pages: 21
DOI: 10.4018/IJWSR.2018070105
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This article describes how understanding human mobility behavior is of great significance for predicting a broad range of socioeconomic phenomena in contemporary society. Although many studies have been conducted to uncover behavioral patterns of intra-urban and inter-urban human mobility, a fundamental question remains unanswered: To what degree is human mobility behavior predictable in new cities—a person has never visited before? Location-based social networks with a large volume of check-in records provide an unprecedented opportunity to investigate cross-urban human mobility. The authors' empirical study on millions of records from Foursquare reveals the motives and behavioral patterns of non-natives in 59 cities across the United States. Inspired by the ideology of transfer learning, the authors also propose a machine learning model, which is designed based on the regularities that they found in this study, to predict cross-urban human whereabouts after non-natives move to new cities. The experimental results validate the effectiveness and efficiency of the proposed model, thus allowing us to predict and control activities driven by cross-urban human mobility, such as mobile recommendation, visual (personal) assistant, and epidemic prevention.
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1. Introduction

Predicting human mobility is a fundamental question for a broad range of applications (Song et al., 2010), including urban planning (Yuan et al., 2012), epidemic forecasting (Dalziel et al., 2013), product advertising (Kirchner et al., 2012) and so on. It is vitally significant to understand socioeconomic phenomena embodying spatiality and human movement by unfolding human mobility patterns (Yan et al., 2013). Therefore, numerous researchers have attempted to uncover and model human mobility patterns (Gonzalez et al., 2008; Noulas et al., 2012; Hasan et al., 2013a; Schneider et al., 2013; Barchiesi et al., 2015; Pappalardo et al., 2015; Gallotti et al., 2016), to provide a deeper understanding of individual and collective mobility behaviors. In recent years, the rapid advances in mobile computing and social networking services empower people to share and use their locations and location-related content in location-based social networks (LBSNs) (Bao et al., 2015) such as Foursquare ( and Gowalla1. As a new type of data source containing extensive geo-tagged data with high position resolution and at large spatial scales, these LBSNs have enabled more exciting and critical studies on human mobility than those traditional ways of mobile phone and taxi (Noulas et al., 2012; Barchiesi et al., 2015; Hasan et al., 2013b; Wang & Stefanone, 2013; Wu et al., 2014; Zhang et al., 2015a; Zhang et al., 2015b; Huang & Wong, 2015; Hess et al., 2016).

In statistical physics and computer science, most of the previous studies conducted on large-scale check-in data from LBSNs focused mainly on intra-urban human mobility patterns and dynamics. In addition to the models proposed in statistical physics (Noulas et al., 2012; Hasan et al., 2013a; Schneider et al., 2013; Wu et al., 2014; Huang & Wong, 2015), machine learning based prediction models for points of interest (POIs) have attracted much attention, along with the increasing popularity of recommender systems (Hess et al., 2016). To the best of our knowledge, many types of information are considered as features when training such prediction models (Bao et al., 2015; Hess et al., 2016). These features include user’s check-in history, content information (such as POI properties, user preferences, and sentiment indications) (Gao et al., 2015; Majid et al., 2013), the geographical influence based on the distribution of geographic distance (Ye et al., 2011; Liu et al., 2015), temporal influence (Yuan et al., 2013; Hosseini & Li, 2016), and social ties between friends (Ye et al., 2011; Zhou et al., 2015; Huang et al., 2015). In particular, a few elaborate hybrid models (Zhang et al., 2015b; Gao et al., 2015; Liu et al., 2015; Hosseini & Li, 2016; Huang et al., 2015; Yin et al., 2016), which utilize various combinations of the information mentioned above, have also been made to achieve higher recommendation accuracy for commercial purposes.

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