基于游览行为的游客偏好学习方法

Tourists’ preference learning method based on travel behavior

针对目前游客偏好学习方法无法获取特定景区内的细粒度客观游客偏好问题,提出一种基于游览行为的游客偏好学习方法。通过结合iBeacon并利用智能手机中的相机、加速度传感器采集游客真实的行为数据,获取游客在景区内各游览点的拍照次数、游玩时间、停留次数等多种游览行为,通过偏好学习模型获取游客偏好。与传统的通过评分、评价获取游客偏好的方法相比,该方法可准确客观获取游客对于景区中游览点的喜好度。

At present,the methods of tourist preference learning cannot obtain fine-grained and objective tourist preference.In response to this situation,a tourist preference learning method based on tour behaviors was proposed,which used the camera and acceleration sensor in smart phone and iBeacon to collect the objective real behavior data of the tourists,a variety of behaviors was analyzed such as the amount of photos and the enjoying time of the tourists at the point of the scenic spots,which were used to obtain tourist preferences using apreference learning model.Compared with the traditional methods of learning tourists’ preferences by scoring and evaluating,the proposed method obtains the tourists’ preference for the scenic spots more accurately.