Intention-Based Destination Recommendation in Navigation Systems

Taking natural languages as inputs has been widely used in applications. Since the navigation application, which one of the fundamental and most used applications, is usually used during driving, thus the navigation application to take natural language as inputs can reduce the risk of driving. In reality, people use different ways to express their moving intentions. For example, ‘I want to go to the bank’ and ‘I want to cash check’ both reveal that the user wants to go to the bank. So we propose a new navigation system to take natural languages as inputs and recommend destinations to users by detecting users’ moving intentions. The navigation system firstly utilizes a wealth of check-in data to extract corresponding words, including stuff and actions, for different types of locations. Then the extracted information is used to detect users’ moving intentions and recommend suitable destinations. We formalize this task as a problem of constructing a model to classify users’ input sentences into location types, and finding proper destinations to recommend. For empirical study, we conduct extensive experiments based on real datasets to evaluate the performance and effectiveness of our navigation system.