Destination prediction is an essential task in a variety of mobile applications. In this paper, we optimize the matrix operation and adapt a semi-lazy framework to improve the prediction accuracy and efficiency of a state-of-the-art approach. To this end, we employ efficient dynamic-programming by devising several data constructs including Efficient Transition Probability and Transition Probabilities with Detours that are capable of pinpointing the minimum amount of computation. We prove that our method achieves one order of cut in both time and space complexity. The experimental results on real-world and synthetic datasets have shown that our solution consistently outperforms its state-of-the-art counterparts in terms of both efficiency (approximately over 100 times faster) and accuracy (above 30 increase).
A majority of the aforementioned research focuses on one or several geo locations that matter most, either the current position or some statistically significant places, and then perceive them as several discrete states of a Markov model or a HMM @cite_10 @cite_17 @cite_4 @cite_22 @cite_7 @cite_19 .
@cite_4 @cite_22 @cite_7 @cite_19 @cite_10 @cite_17
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The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc. In this paper we focus on the locations explicit and implicit in users descriptions of their surroundings. We propose a statistical language modeling approach to identifying locations in arbitrary text, and investigate several ways to estimate the models, based on the term frequency and the user frequency. The geotagged public photos in Flickr serve as a convenient ground truth. Our results show that we can predict location within a one kilometer by one kilometer cell with 17 accuracy, and within a three kilometer radius around such a one kilometer cell with 40 accuracy, using only a photo's tags. This is significantly better than the state of the art. Further we examine several estimation strategies that leverage the physical proximity of places, and show that for sparsely represented locations, smoothing from the immediate neighborhood improves results. We also show that estimation strategies based on user frequency are much more reliable than approaches based on the raw term frequency. When monitoring spatial phenomena, such as the ecological condition of a river, deciding where to make observations is a challenging task. In these settings, a fundamental question is when an active learning, or sequential design, strategy, where locations are selected based on previous measurements, will perform significantly better than sensing at an a priori specified set of locations. For Gaussian Processes (GPs), which often accurately model spatial phenomena, we present an analysis and efficient algorithms that address this question. Central to our analysis is a theoretical bound which quantifies the performance difference between active and a priori design strategies. We consider GPs with unknown kernel parameters and present a nonmyopic approach for trading off exploration, i.e., decreasing uncertainty about the model parameters, and exploitation, i.e., near-optimally selecting observations when the parameters are (approximately) known. We discuss several exploration strategies, and present logarithmic sample complexity bounds for the exploration phase. We then extend our algorithm to handle nonstationary GPs exploiting local structure in the model. We also present extensive empirical evaluation on several real-world problems. Our previous work [1] defined the notion of perfect location privacy for location-based services (LBS) and demon­strated how it can be achieved under basic user models. In this paper, we extend our work by studying perfect location privacy within a more complex scenario in which a user's movements are dependent upon the time domain. We model a mobile user's path using Markov chains and show that perfect location privacy is achievable for the user if her pseudonym is changed before o(n 2 |E|-r) observations by the adversary, where |E| is the number of edges in the Markov model and r is the number of all possible locations. We support our results with simulations.