2010 AnEnergyEfficientMobileRecommen

• (Ge et al., 2010) ⇒ Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, and Michael Pazzani. (2010). “An Energy-efficient Mobile Recommender System.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835918

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• http://scholar.google.com/scholar?q=%22An+energy-efficient+mobile+recommender+system%22+2010
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Quotes

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 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 AnEnergyEfficientMobileRecommen	Michael J. Pazzani Hui Xiong Alexander Tuzhilin Keli Xiao Marco Gruteser Yong Ge			An Energy-efficient Mobile Recommender System		KDD-2010 Proceedings	http://pegasus.rutgers.edu/~yongge/KDD10-MRS.pdf	10.1145/1835804.1835918		2010