We enhance the mobile sequential recommendation (MSR) model and address some critical issues in existing formulations by proposing three new forms of the MSR from a multi-user perspective. The multi-user MSR (MMSR) model searches optimal routes for multiple drivers at different locations while disallowing overlapping routes to be recommended. To enrich the properties of pick-up points in the problem formulation, we additionally consider the pick-up capacity as an important feature, leading to the following two modified forms of the MMSR: MMSR-m and MMSR-d. The MMSR-m sets a maximum pick-up capacity for all urban areas, while the MMSR-d allows the pick-up capacity to vary at different locations. We develop a parallel framework based on the simulated annealing to numerically solve the MMSR problem series. Also, a push-point method is introduced to improve our algorithms further for the MMSR-m and the MMSR-d, which can handle the route optimization in more practical ways. Our results on both real-world and synthetic data confirmed the superiority of our problem formulation and solutions under more demanding practical scenarios over several published benchmarks.

中文翻译：

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用于路由优化的多用户移动顺序推荐

我们通过从多用户的角度提出三种新形式的 MSR 来增强移动顺序推荐 (MSR) 模型并解决现有公式中的一些关键问题。多用户 MSR (MMSR) 模型为不同位置的多个驾驶员搜索最佳路线，同时不允许推荐重叠路线。为了丰富问题表述中拾取点的属性，我们另外将拾取能力视为一个重要特征，从而产生了以下两种修改形式的 MMSR：MMSR-m 和 MMSR-d。MMSR-m 为所有城市地区设置了最大接送能力，而 MMSR-d 允许接送能力在不同地点有所不同。我们开发了一个基于模拟退火的并行框架来数值求解 MMSR 问题系列。还，引入了push-point方法来进一步改进我们的MMSR-m和MMSR-d算法，可以更实际地处理路由优化。我们对真实世界和合成数据的结果证实了我们的问题制定和解决方案在更苛刻的实际场景下优于几个已发布的基准。