Route recommendation service has become a big business in industry since traveling is now an important part of our daily life. We can travel to unknown places by simply typing in our destination and then following recommendation service's guidance, that a pleasant trip desires them to provide a good route. However, previous research shows that even the routes recommended by the big-thumb service providers can deviate significantly from the routes travelled by experienced drivers since the many latent factors affect drivers' preferences and it is hard for a single route recommendation algorithm to model all of them. In this demo we will present the CrowPlanner system to leverage crowds' knowledge to improve the recommendation quality. It requests human workers to evaluate candidates routes recommended by different sources and methods, and determines the best route based on the feedbacks of these workers. In this demo, we first introduce the core component of our system for smart question generation, and then show several real route recommendation cases and the feedback of users.
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