DeepAltTrip：Top-k备选行程推荐
DeepAltTrip: Top-k Alternative Itineraries for Trip Recommendation
摘要：
行程行程推荐从城市中的大量候选兴趣点（POI）中找到有序的兴趣点序列。在本文中，我们提出了一个基于深度学习的框架，称为DeepAltTrip，它学习为给定的源和目的地POI推荐前k个替代行程。这些替代路线将不仅是受欢迎的，因为过去的用户采用的历史路线，而且彼此不同（或不同）。DeepAltTrip由两个主要组件组成：（i）行程网络（ITRNet），它通过使用图形自动编码器和两个（前向和后向）LSTM来估计行程上POI的可能性;以及（ii）路线生成过程，用于生成通过使用ITRNet获得的相关POI的k个不同行程。对于路线生成步骤，我们提出了一种新的采样算法，可以无缝地处理各种各样的用户定义的约束。据我们所知，这是第一个从历史旅行中学习的工作，为用户提供了一套可供选择的行程。在八个流行的真实世界数据集上进行的广泛实验表明，我们的方法比最先进的方法更有效。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
