A Mobile Services Collaborative Recommendation Algorithm Based on Location-Aware Hidden Markov Model
Nowadays, location based services (LBS) has become one of the most popular applications with the rapid development of mobile Internet environment. 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 likely service. Furthermore, it integrates the generated LHMM, user’s score and interest migration into the traditional CF algorithm to generate a final recommendation list. The LHMM-based CF algorithm mixes the geographic factors and personalized behavior and experimental results show that it has more accuracy than other state-of-the-arts algorithms.
KeywordsBehavior prediction LBS LHMM Collaborative recommendation
This work is partially supported by National Natural Science Foundation of China (61074135, 61303096, 71101086) and Shanghai Leading Academic Discipline Project (J50103). We also would like to show our great appreciations to all of our hard working fellows in the projects above.
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