Social link prediction using category based location history in trajectory data
DOI:
https://doi.org/10.26438/ijcse/v5i11.167170Keywords:
Trajectory, contextual information, social link predictionAbstract
With the rising popularity of location-based services, trajectory data mining became an important research topic. There exists many data mining algorithms for systematic processing, managing and mining of trajectory data. Trajectory data mining has many applications such as location recommandations, social link prediction, movement behaviour analysis etc. Here proposes a contextual trajectory analysis model which provides a flexible way to characterize the complex moving nature of humans. It embed multiple contextual information for efficiently modeling data. It includes user-level, trajectory-level, location-level, and temporal-level contexts. It can be used to predict the future location of a user based on the previous travelling pattern. Social link prediction aims to find out whether there exists reciprocal link between two users. Here also propose a method for social link prediction from trajectory data by analyzing the nearest neighbour. This method considers the tf-idf metrics as the baseline.
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