Tour itinerary planning and recommendation are challenging tasks for tourists in unfamiliar countries. Many tour recommenders only consider broad POI categories and do not align well with users' preferences and other locational constraints. We propose an algorithm to recommend personalized tours using POI-embedding methods, which provides a finer representation of POI types. Our recommendation algorithm will generate a sequence of POIs that optimizes time and locational constraints, as well as user's preferences based on past trajectories from similar tourists. Our tour recommendation algorithm is modelled as a word embedding model in natural language processing, coupled with an iterative algorithm for generating itineraries that satisfies time constraints. Using a Flickr dataset of 4 cities, preliminary experimental results show that our algorithm is able to recommend a relevant and accurate itinerary, based on measures of recall, precision and F1-scores.

翻译：对不熟悉国家的游客来说,旅游行程规划和建议是具有挑战性的任务。许多旅游推荐人只考虑广义的POI类别,与用户的偏好和其他地点限制不相符。我们建议一种算法,用POI编组方法推荐个性化旅游,这种方法更精细地代表了POI的类型。我们的建议算法将产生一系列的POI,以优化时间和地点限制,以及基于类似游客过去轨迹的用户偏好。我们的旅游推荐算法仿照了自然语言处理中的词嵌入模式,加上一种满足时间限制的迭代算法,利用4个城市的Flickr数据集,初步实验结果显示,我们的算法能够根据记号、精确度和F1分数的计量,提出相关和准确的行程。