Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only. However, there are other factors associated with parking spaces that can influence someone's choice of parking such as fare, parking rule, walking distance to destination, travel time, likelihood to be unoccupied at a given time. More importantly, these factors may change over time and conflict with each other which makes the recommendations produced by current parking recommender systems ineffective. In this paper, we propose a novel problem called multi-objective parking recommendation. We present a solution by designing a multi-objective parking recommendation engine called MoParkeR that considers various conflicting factors together. Specifically, we utilise a non-dominated sorting technique to calculate a set of Pareto-optimal solutions, consisting of recommended trade-off parking spots. We conduct extensive experiments using two real-world datasets to show the applicability of our multi-objective recommendation methodology.

翻译：现有停车建议解决方案主要侧重于根据空置选项寻找和提出泊车位建议,然而,与停车位相关的其他因素可能影响到某人选择停车位,如票价、停车规则、步行到目的地的距离、旅行时间、在特定时间无人占用的可能性等。更重要的是,这些因素可能随时间而变化,彼此冲突,使目前的停车建议系统产生的建议无效。在本文件中,我们提出了一个新问题,称为多目标停车建议。我们通过设计一个多目标的停车建议引擎,称为MOParkeR,考虑各种相互冲突的因素,提出了一个解决方案。具体地说,我们使用一种非主导式的排序技术来计算一套“最佳”解决方案,其中包括推荐的停车点。我们利用两个现实世界数据集进行广泛的实验,以显示我们多目标建议方法的适用性。