TEMMUS: A Mobility Predictor based on Temporal Markov Model with User Similarity
Location-Based Social Networks (LBSN) data contains spatial, temporal, and social features of user activity, providing valuable information that is currently available on large-scale and low-cost fashion via traditional data collection methods. In this way, LBSN data enables to predict user mobility based on spatial, temporal, and social features, which can be used in several areas, such as, device-to-device(D2D) communication, cache, and others. In addition, a Temporal Markov Chain (TMC) is a stochastic model used to model randomly changing systems, such as mobility prediction based on the spatiotemporal factor such as location and day of the week. In this paper, we introduce the TEmporal Markov Model with User Similarity (TEMMUS) mobility prediction model. TEMMUS considers a TMC of variable order based on the day of the week (weekday or weekend) and the user similarity to predict the users future location. The results highlight a higher accuracy of TEMMUS compared to three state-of-the-art Markov Model predictors.
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