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

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

Mingjun Xin (School of Computer Engineering and Science, Shanghai University, Shanghai, China), Yanhui Zhang (School of Computer Engineering and Science, Shanghai University, Shanghai, China), Shunxiang Li (School of Computer Engineering and Science, Shanghai University, Shanghai, China), Liyuan Zhou (School of Computer Engineering and Science, Shanghai University, Shanghai, China), and Weimin Li (School of Computer Engineering and Science, Shanghai University, Shanghai, China)
Copyright: © 2017 |Pages: 22
DOI: 10.4018/IJWSR.2017040103
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Abstract

Nowadays, location based services (LBS) has become one of the most popular applications with the rapid development of mobile Internet technology. More and more research is focused on discovering the required services among massive information according to the personalized behavior. In this paper, a collaborative filtering (CF) recommendation algorithm is presented based on the Location-aware Hidden Markov Model (LHMM). This approach includes three main stages. First, it clusters users by making a pattern similarity calculation of their historical check-in data. Then, it establishes the location-aware transfer matrix so as to get the next most similar service. Furthermore, it integrates the generated LHMM, user's score and interest migration into the traditional CF algorithm so as to generate a final recommendation list. The LHMM-based CF algorithm mixes the geographic factors and personalized behavior and experimental results show that it outperforms the state-of-the-art algorithms on both precision and recall.
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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.

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