TY - JOUR
T1 - FineRoute
T2 - Personalized and Time-Aware Route Recommendation Based on Check-Ins
AU - Zhu, Xiaoyan
AU - Hao, Ripei
AU - Chi, Haotian
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11
Y1 - 2017/11
N2 - The rapid expansion of urbanization and the fast pace of life result in abundant choices with little time for people to manage routes. A proper planning route enables us to enjoy life better with fewer time and energy costs. Therefore, route planning becomes too valuable to be ignored. At the same time, with the popularity of mobile devices, location sensing, and Web 2.0 technologies, location-based social networks (e.g., Facebook Palaces and Foursquare) have attracted millions of users to share their visited locations and other information, which generates large amounts of user check-in data. These data can be used to mine users' preferences and time information for recommending routes. In this paper, we propose FineRoute, a personalized and time-sensitive route recommendation system. We take three factors users' preferences, proper visiting time, and transition time into consideration for the route generation. First, we infer users' preferences by constructing a three-dimensional tensor, with three dimensions representing users, locations, and time, respectively. Second, we obtain the proper visiting time for certain locations, as well as the transition time between two locations from the check-in dataset. Moreover, we adopt Kullback-Leibler divergence in order to measure the quality of a route in terms of the proper visiting month and the proper visiting hour. Finally, we propose a route generation algorithm by extending the classic longest path algorithm. We conduct experiments on a real-world check-in dataset and the results demonstrate the effectiveness of our scheme.
AB - The rapid expansion of urbanization and the fast pace of life result in abundant choices with little time for people to manage routes. A proper planning route enables us to enjoy life better with fewer time and energy costs. Therefore, route planning becomes too valuable to be ignored. At the same time, with the popularity of mobile devices, location sensing, and Web 2.0 technologies, location-based social networks (e.g., Facebook Palaces and Foursquare) have attracted millions of users to share their visited locations and other information, which generates large amounts of user check-in data. These data can be used to mine users' preferences and time information for recommending routes. In this paper, we propose FineRoute, a personalized and time-sensitive route recommendation system. We take three factors users' preferences, proper visiting time, and transition time into consideration for the route generation. First, we infer users' preferences by constructing a three-dimensional tensor, with three dimensions representing users, locations, and time, respectively. Second, we obtain the proper visiting time for certain locations, as well as the transition time between two locations from the check-in dataset. Moreover, we adopt Kullback-Leibler divergence in order to measure the quality of a route in terms of the proper visiting month and the proper visiting hour. Finally, we propose a route generation algorithm by extending the classic longest path algorithm. We conduct experiments on a real-world check-in dataset and the results demonstrate the effectiveness of our scheme.
KW - Check-in data
KW - location-based social networks
KW - route recommendation
KW - tensor
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U2 - 10.1109/TVT.2017.2764999
DO - 10.1109/TVT.2017.2764999
M3 - Article
AN - SCOPUS:85032662160
SN - 0018-9545
VL - 66
SP - 10461
EP - 10469
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 11
M1 - 8078272
ER -