Trip recommender system, which targets at recommending a trip consisting of several ordered Points of Interest (POIs), has long been treated as an important application for many location-based services. Currently, most prior arts generate trips following pre-defined objectives based on constraint programming, which may fail to reflect the complex latent patterns hidden in the human mobility data. And most of these methods are usually difficult to respond in real time when the number of POIs is large. To that end, we propose an Adversarial Neural Trip Recommendation (ANT) framework to tackle the above challenges. First of all, we devise a novel attention-based encoder-decoder trip generator that can learn the correlations among POIs and generate well-designed trips under given constraints. Another novelty of ANT relies on an adversarial learning strategy integrating with reinforcement learning to guide the trip generator to produce high-quality trips. For this purpose, we introduce a discriminator, which distinguishes the generated trips from real-life trips taken by users, to provide reward signals to optimize the generator. Moreover, we devise a novel pre-train schema based on learning from demonstration, which speeds up the convergence to achieve a sufficient-and-efficient training process. Extensive experiments on four real-world datasets validate the effectiveness and efficiency of our proposed ANT framework, which demonstrates that ANT could remarkably outperform the state-of-the-art baselines with short response time.
翻译:远足推荐系统,其目标在于建议由多个定购利益点组成的旅行,长期以来一直被视为许多基于地点的服务的重要应用。目前,大多数先行艺术根据限制程序制定预先确定的目标,产生旅行,这可能无法反映人类流动数据中隐藏的复杂潜在模式。而且这些方法大多通常难以实时响应,当污染物数量巨大时。为此,我们提议了一个逆向神经旅行建议(ANT)框架,以应对上述挑战。首先,我们设计了一个以关注为基础的新关注编码-脱coder旅行生成器,可以学习同源码之间的关联,并在有限制的情况下产生设计良好的旅行。另外一个新的先行艺术根据对抗性学习战略,结合强化性学习,引导出行者进行高质量的旅行。为此,我们引入了一种区分出行与用户进行的实际生活旅行的区别,为优化发电机提供奖励性响应信号。此外,我们根据从演示中学习的短期时间框架设计了一个新的先行前计划,可以学习同源系统之间的关系,加快了升级的进度,并展示了我们所提议的全球基准框架。