Predicting new and unusual mobility patterns

Traditional location-based service profiles user's traits by looking for patterns in historical mobility behaviors. Yet, from time to time, people are adventurous and would often like to go to unvisited places, or follow new transition paths. At that time, their next movements will be inconsistent with any previous patterns, making location-based recommendations inaccurate and irrelevant to user's real need. Under such circumstance, an alternative strategy is to figure out user's destination and intention before recommendation, where the ability to predict new and unusual mobility patterns plays a critical role. In this paper, we define the next location that breaks the earliest on-going patterns as a Point of Change (POC). To predict POCs, we introduce a mobility model, called ST-Pattern Network, to learn the occurrences of POCs under the regularity of spatial-temporal patterns. By computing the similarities of matched patterns, our model can online predict future POCs as well as fit recent trajectory via a pattern network. Experiments show 10% accuracy improvement on POC prediction can be achieved over traditional Markov models. Furthermore, we are able to categorize the POC into more refined scenarios so that different recommendations can be suggested under different circumstances.