We Know Your Preferences in New Cities: Mining and Modeling the Behavior of Travelers

Rong Xie; Yang Chen; Qinge Xie; Yu Xiao; Xin Wang

doi:10.1109/MCOM.2018.1700581

We Know Your Preferences in New Cities: Mining and Modeling the Behavior of Travelers

Rong Xie, Yang Chen, Qinge Xie, Yu Xiao, Xin Wang

Mobile Cloud Computing

Fudan University

Research output: Contribution to journal › Article › Scientific › peer-review

8 Citations (Scopus)

267 Downloads (Pure)

Abstract

The trend of globalization motivates people to travel more often to different cities. In order to provide better suggestions for travelers, it is important to understand their preferences for venue types. In this article, we investigate travelers’ preferences based on the check-in data collected from a popular Location-Based Social Application called Swarm. We conduct a thorough analysis of the check-in data to discover the variation in travelers’ preferences between cities with different characteristics, and to build a model for predicting the venue types of travelers’ interests in each city. Our experimental results demonstrate that the F1-score increases by 0.19 by taking into account the characteristics of the destination city. Moreover, our approach outperforms collaborative filtering, a widely used approach to the design of recommendation systems.

Original language	English
Article number	8539017
Pages (from-to)	28-35
Number of pages	8
Journal	IEEE Communications Magazine
Volume	56
Issue number	11
DOIs	https://doi.org/10.1109/MCOM.2018.1700581
Publication status	Published - 2018
MoE publication type	A1 Journal article-refereed

Keywords

location-based social applications
Swarm
check-in
venue type
traveler behavior

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10.1109/MCOM.2018.1700581

human_behavior_mining_in_urban_environmentAccepted author manuscript, 5.09 MB

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Yang Chen
Yu Xiao (Host)
27 Aug 2018 → 7 Sept 2018
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title = "We Know Your Preferences in New Cities: Mining and Modeling the Behavior of Travelers",

abstract = "The trend of globalization motivates people to travel more often to different cities. In order to provide better suggestions for travelers, it is important to understand their preferences for venue types. In this article, we investigate travelers{\textquoteright} preferences based on the check-in data collected from a popular Location-Based Social Application called Swarm. We conduct a thorough analysis of the check-in data to discover the variation in travelers{\textquoteright} preferences between cities with different characteristics, and to build a model for predicting the venue types of travelers{\textquoteright} interests in each city. Our experimental results demonstrate that the F1-score increases by 0.19 by taking into account the characteristics of the destination city. Moreover, our approach outperforms collaborative filtering, a widely used approach to the design of recommendation systems.",

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We Know Your Preferences in New Cities: Mining and Modeling the Behavior of Travelers

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