Backward Path Growth for Efficient Mobile Sequential Recommendation

• Published in: IEEE transactions on knowledge and data engineering Vol. 27; no. 1; pp. 46 - 60
• Main Authors: Huang, Jianbin, Huangfu, Xuejun, Sun, Heli, Li, Hui, Zhao, Peixiang, Cheng, Hong, Song, Qinbao
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2015
• Subjects: backward path growth; Computational complexity; Educational institutions; Mobile communication; Mobile sequential recommendation; potential travel distance; Search problems; sequence pruning; Trajectory; Vectors; Vehicles
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2014.2298012

The problem of mobile sequential recommendation is to suggest a route connecting a set of pick-up points for a taxi driver so that he/she is more likely to get passengers with less travel cost. Essentially, a key challenge of this problem is its high computational complexity. In this paper, we propose a novel dynamic programming based method to solve the mobile sequential recommendation problem consisting of two separate stages: an offline pre-processing stage and an online search stage. The offline stage pre-computes potential candidate sequences from a set of pick-up points. A backward incremental sequence generation algorithm is proposed based on the identified iterative property of the cost function. Simultaneously, an incremental pruning policy is adopted in the process of sequence generation to reduce the search space of the potential sequences effectively. In addition, a batch pruning algorithm is further applied to the generated potential sequences to remove some non-optimal sequences of a given length. Since the pruning effectiveness keeps growing with the increase of the sequence length, at the online stage, our method can efficiently find the optimal driving route for an unloaded taxi in the remaining candidate sequences. Moreover, our method can handle the problem of optimal route search with a maximum cruising distance or a destination constraint. Experimental results on real and synthetic data sets show that both the pruning ability and the efficiency of our method surpass the state-of-the-art methods. Our techniques can therefore be effectively employed to address the problem of mobile sequential recommendation with many pick-up points in real-world applications.