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.
Further, for new users, the training data is typically insufficient and unavailable, which lead to a poor predictive performance, so Mclnerney presented a framework to enhance prediction using information about the mobility habits of existing users REF .
Improving location prediction services for new users with probabilistic latent semantic analysis
UbiComp '12
Computer Science