Improving location prediction services for new users with probabilistic latent semantic analysis


McInerney, James, Rogers, Alex and Jennings, Nicholas R. (2012) Improving location prediction services for new users with probabilistic latent semantic analysis At 4th International Workshop on Location-Based Social Networks, United States. 08 Sep 2012. 5 pp. (doi:10.1145/2370216.2370420).

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Description/Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Digital Object Identifier (DOI): doi:10.1145/2370216.2370420
Venue - Dates: 4th International Workshop on Location-Based Social Networks, United States, 2012-09-08 - 2012-09-08
Keywords: knowledge representation and reasoning, geometric, spatial, and temporal reasoning, machine learning, data mining, reasoning under uncertainty, uncertainty in ai, unsupervised learning
Subjects: Q Science > QA Mathematics > QA76 Computer software
H Social Sciences > HE Transportation and Communications
Organisations: Agents, Interactions & Complexity
ePrint ID: 342584
Date :
Date Event
8 September 2012Published
Date Deposited: 10 Sep 2012 13:57
Last Modified: 17 Apr 2017 16:39
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/342584

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