Abstract
In recent years, according to the rapid growth of the smart devices, people use different applications on the smart devices to find restaurants, coupons, even to record their weekly time table and to check the real-time traffic status; from above tells that applications on the smart devices have became an indispensable thing for people in nowadays. The question for us is ”How do we find our value from all these different kind of applications?” The application we present not only provides restaurant recommendation system, recommending high-reviews restaurants for users, but also provides personal behavior records for users to review every weekly details and even get to plan and manage the time of use. In this paper, we focus more on the restaurant recommendation system, recommendation system is a popular and common topic. The biggest task is how to recommend high-reviews restaurants in a motley variety of restaurants. We wish to decide high evaluation restaurants based on analyze the articles of food. First, the jieba Chinese text segmentation was employed to accomplish the text segmentation task. Then, we are training lexicon about foods, and analyze an article by using support vector machine (SVM) algorithm and intuitive method. Finally, we use the results of the analysis as the grade basis. In the following chapters, we will describe application framework in detail including the user interface of application, algorithm, system architecture, and simulation results.