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.
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