An energy-efficient mobile recommender system

Yong Ge, Hui Xiong, A. Tuzhilin, Keli Xiao, M. Gruteser, M. Pazzani
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引用次数: 369

Abstract

The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (green knowledge), which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors. However, extracting green knowledge from location traces is not a trivial task. Conventional data analysis tools are usually not customized for handling the massive quantity, complex, dynamic, and distributed nature of location traces. To that end, in this paper, we provide a focused study of extracting energy-efficient transportation patterns from location traces. Specifically, we have the initial focus on a sequence of mobile recommendations. As a case study, we develop a mobile recommender system which has the ability in recommending a sequence of pick-up points for taxi drivers or a sequence of potential parking positions. The goal of this mobile recommendation system is to maximize the probability of business success. Along this line, we provide a Potential Travel Distance (PTD) function for evaluating each candidate sequence. This PTD function possesses a monotone property which can be used to effectively prune the search space. Based on this PTD function, we develop two algorithms, LCP and SkyRoute, for finding the recommended routes. Finally, experimental results show that the proposed system can provide effective mobile sequential recommendation and the knowledge extracted from location traces can be used for coaching drivers and leading to the efficient use of energy.
高效节能的移动推荐系统
大规模位置轨迹的日益可用性为改变交通系统中知识发现的范式创造了前所未有的机会。一个特别有希望的领域是提取能源效率高的运输模式(绿色知识),这可以作为减少运输部门能源消耗效率低下的指导。然而,从位置轨迹中提取绿色知识并不是一项简单的任务。传统的数据分析工具通常不适合处理大量、复杂、动态和分布式的位置痕迹。为此,本文重点研究了从位置轨迹中提取节能交通模式的方法。具体来说,我们最初的重点是一系列移动推荐。作为一个案例研究,我们开发了一个移动推荐系统,该系统能够为出租车司机推荐一系列的上车点或一系列潜在的停车位置。这个移动推荐系统的目标是最大化商业成功的概率。沿着这条线,我们提供了一个潜在传播距离(PTD)函数来评估每个候选序列。该PTD函数具有单调性,可以有效地对搜索空间进行修剪。基于这个PTD函数,我们开发了LCP和SkyRoute两种算法来寻找推荐路由。最后,实验结果表明,该系统可以提供有效的移动顺序推荐,并且从位置轨迹中提取的知识可以用于指导驾驶员,从而有效地利用能量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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