路由自适应移动连续推荐的逆向方法开发

Mrs. S. Nithya, S. Visakh, J. Gollner, C. Ashley
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引用次数: 0

摘要

发展是旅行者的预先规划的必要条件。所以他们更多地接触到我们的出租车服务。此外,我们提出了一种新的基于动态规划的方法来解决移动序列推荐问题,该算法名为UniBic,在窄双聚类上,除了BicSPAM之外,在常用的评估场景中优于以往的所有双聚类算法。同时,序列生成过程有效地减少了潜在序列的搜索空间。此外,该方法还可以处理具有最大巡航距离或目的地约束的最优路线搜索问题。在真实数据集和合成数据集上的实验结果表明,该方法的剪枝能力和剪枝效率都超过了目前最先进的方法。因此,我们的技术可以有效地用于解决现实应用中具有许多拾取点的移动顺序推荐问题
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Backward Approach Development for Route Adaptive Mobile Successive Recommendation
The Backward Approach Development is the pre-planning requisite for the traveller’s.So they are more exposed to our taxi service in and out. In addition, accurate query results with up-to-date travel times, price etc.we propose a novel dynamic programming based method to solve the mobile sequential recommendation problem with the new algorithm, named UniBic, outperformed all previous biclustering algorithms in terms of commonly used evaluation scenarios except for BicSPAM on narrow biclusters Simultaneously, the process of sequence generation to reduce the search space of the potential sequences effectively. 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 pickup points in real-world applications
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