Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Interest (POIs) but their solutions may not align well with the user's own preferences and other location constraints. Additionally, these solutions do not take into consideration of the users' preference based on their past POIs selection. In this paper, we propose POIBERT, an algorithm for recommending personalized itineraries using the BERT language model on POIs. POIBERT builds upon the highly successful BERT language model with the novel adaptation of a language model to our itinerary recommendation task, alongside an iterative approach to generate consecutive POIs. Our recommendation algorithm is able to generate a sequence of POIs that optimizes time and users' preference in POI categories based on past trajectories from similar tourists. Our tour recommendation algorithm is modeled by adapting the itinerary recommendation problem to the sentence completion problem in natural language processing (NLP). We also innovate an iterative algorithm to generate travel itineraries that satisfies the time constraints which is most likely from past trajectories. Using a Flickr dataset of seven cities, experimental results show that our algorithm out-performs many sequence prediction algorithms based on measures in recall, precision and F1-scores.

翻译：旅游行程规划和建议是访问不熟悉城市的游客面临的棘手问题。许多旅游建议算法只考虑兴趣点的位置和广度等因素,如兴趣点的位置和广度等,但其解决办法可能与用户本身的偏好和其他地点限制不相符。此外,这些解决办法没有考虑到用户根据过去对PoI的选择而偏爱的用户。在本文中,我们建议使用PoI/PoI/BERT语言模型推荐个性化路线的算法,POIBERT是一种推荐个人化路线的算法。POIBERT以高度成功的BERT语言模型为基础,对我们行程建议任务的语言模式进行了新调整,同时采用迭接方法生成连续的 POIs。我们的建议算法能够产生一系列的POIs,根据类似游客过去的轨迹优化POI类别的时间和用户的偏好。我们的旅游建议算法是将行程建议问题与自然语言处理中的句尾补问题(NLPP)相适应。我们还发明了一种迭代算法,以产生满足最有可能从过去的轨迹轨迹上排出的时间限制的旅游。我们七个城市的Flicklex-Agaslationalslationalsmas