Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
LeZi-update: an information-theoretic approach to track mobile users in PCS networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data
IEEE Transactions on Mobile Computing
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Proceedings of the 13th international conference on Ubiquitous computing
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Forecasting multi-appliance usage for smart home energy management
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Location prediction systems that attempt to determine the mobility patterns of individuals in their daily lives have become increasingly common in recent years. Approaches to this prediction task include eigenvalue decomposition [5], non-linear time series analysis of arrival times [10], and variable order Markov models [1]. However, these approaches all assume sufficient sets of training data. For new users, by definition, this data is typically not available, leading to poor predictive performance. Given that mobility is a highly personal behaviour, this represents a significant barrier to entry. Against this background, we present a novel framework to enhance prediction using information about the mobility habits of existing users. At the core of the framework is a hierarchical Bayesian model, a type of probabilistic semantic analysis [7], representing the intuition that the temporal features of the new user's location habits are likely to be similar to those of an existing user in the system. We evaluate this framework on the real life location habits of 38 users in the Nokia Lausanne dataset, showing that accuracy is improved by 16%, relative to the state of the art, when predicting the next location of new users.