Backward Path Growth for Efficient Mobile Sequential Recommendation

Backward Path Growth for Efficient Mobile Sequential Recommendation

Abstract— Backward Path Growth for Efficient Mobile Sequential Recommendation. 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 < Final Year projects 2016 > 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.