A Location-Context Awareness Mobile Services Collaborative Recommendation Algorithm Based on User Behavior Prediction

2. Related Work And Problem Statement

In this section, some existing research works on HMM prediction and on check in data mining will be reviewed as below.

As for HMM strategies, (Blasiak & Rangwala, 2011) applied HMM to the classification, which completed the sequence classification by combining Baum-Welch, Gibbs sampling and change function together. (Antwarg, & Rokach 2012) used multiple HMM to distinguish user from different devices, ages and gender. Thus, it could provide a more accurate prediction of user's next behavior. With the growing popularity of mobile terminals, HMM also began to be applied to the field of path analysis. (Hamada & Kubo, 2013) used a modified BP-AR-HMM algorithm to predict user's driving behavior under multi-time series. (Wesley 2012) converted earth into a plurality of triangular surface, and completed the prediction of user's next location through putting labeled triangle into HMM learning model.