Point of interest (POI) recommendation is of great value for both service providers and users. However, it is hard due to data scarcity. To this end, in this paper, we propose a transfer learning based deep neural model, which fuses valueable cross-domain knowledge to achieve more accurate POI recommendation. We first learn the user’s spatial and non-spatial preferences based on their historical POI interactions. The model further captures user interactions in other domains and introduces useful preferences into POI recommendations, which can address data sparsity problems. Compared to the matrix factorization based cross-domain techniques, our method utilizes deep transfer learning, which can learn complex user-item interaction relationships and accurately capture user general preferences to transfer. Finally, we evaluate the proposed model using three real-world datasets. The experimental results show that our model significantly outperforms the state-of-the-art approaches for POI recommendation.

中文翻译：

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基于迁移学习的精准POI推荐

兴趣点 (POI) 推荐对服务提供商和用户都具有重要价值。但是，由于数据稀缺，这很难。为此，在本文中，我们提出了一种基于迁移学习的深度神经模型，该模型融合了有价值的跨领域知识以实现更准确的 POI 推荐。我们首先根据用户的历史 POI 交互来了解用户的空间和非空间偏好。该模型进一步捕获其他领域的用户交互，并将有用的偏好引入 POI 推荐，这可以解决数据稀疏问题。与基于矩阵分解的跨域技术相比，我们的方法利用深度迁移学习，可以学习复杂的用户-项目交互关系并准确捕获用户的一般偏好进行迁移。最后，我们使用三个真实世界的数据集评估所提出的模型。实验结果表明，我们的模型明显优于最先进的 POI 推荐方法。