Trip itinerary recommendation finds an ordered sequence of Points-of-Interest (POIs) from a large number of candidate POIs in a city. In this paper, we propose a deep learning-based framework, called DeepAltTrip, that learns to recommend top-k alternative itineraries for given source and destination POIs. These alternative itineraries would be not only popular given the historical routes adopted by past users but also dissimilar (or diverse) to each other. The DeepAltTrip consists of two major components: (i) Itinerary Net (ITRNet) which estimates the likelihood of POIs on an itinerary by using graph autoencoders and two (forward and backward) LSTMs; and (ii) a route generation procedure to generate k diverse itineraries passing through relevant POIs obtained using ITRNet. For the route generation step, we propose a novel sampling algorithm that can seamlessly handle a wide variety of user-defined constraints. To the best of our knowledge, this is the first work that learns from historical trips to provide a set of alternative itineraries to the users. Extensive experiments conducted on eight popular real-world datasets show the effectiveness and efficacy of our approach over state-of-the-art methods.

翻译：在本文中,我们提出了一个深学习框架,叫做DeepAltTrip, 以学习为基础, 学习为特定源代码和目的地源代码和目的源代码建议高端替代路线。这些替代路线不仅由于过去用户采用的历史路线而广受欢迎,而且不同(或不同)地相异。DeepAltTrip由两个主要部分组成:(一) Itinery Net(ITRNet),它利用图解自动编码器和二(前向和后向)LSTMS来估计该路线上源代码的可能性;和(二)一个生成路径生成程序,通过使用 ITRNet 获得的相关轨道序列生成各种不同的路线。关于路径生成步骤,我们建议一种新颖的抽样算法,可以无缝地处理各种用户定义的限制因素。据我们所知,这是从历史旅行中学习的首次工作,以便向用户提供一套替代路径的替代路线,即向用户提供一套(前向和后向)LSTMM(前和后向后)LSTMS;以及(二) 以广度方式展示实际数据效率的方法。