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

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.