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PDPNN: Modeling User Personal Dynamic Preference for Next Point-of-Interest Recommendation

机译：PDPNN：为下一个兴趣点推荐建模用户个人动态偏好

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Next Point of Interest (POI) recommendation is an important aspect of information feeds for Location Based Social Networks (LSBNs). The boom in LSBN platforms such as Foursquare, Twitter, and Yelp has motivated a considerable amount of research focused on POI recommendations within the last decade. Inspired by the success of deep neural networks in many fields, researchers are increasingly interested in using neural networks such as Recurrent Neural Network (RNN) to make POI recommendation. Compared to traditional methods like Factorizing Personalized Markov Chain (FPMC) and Tensor Factorization (TF), neural network methods show great improvement in general sequences prediction. However, the user's personal preference, which is crucial for personalized POI recommendation, is not addressed well in existing works. Moreover, the user's personal preference is dynamic rather than static, which can guide predictions in different temporal and spatial contexts. To this end, we propose a new deep neural network model called Personal Dynamic Preference Neural Network(PDPNN). The core of the PDPNN model includes two parts: one part learns the user's personal long-term preferences from the historical trajectories, and the other part learns the user's short-term preferences from the current trajectory. By introducing a similarity function that evaluates the similarity between spatiotemporal contexts of user's current trajectory and historical trajectories, PDPNN learns the user's personal dynamic preference from user's long-term and short-term preferences. We conducted experiments on three real-world datasets, and the results show that our model outperforms current well-known methods.
机译：下一兴趣点（POI）推荐是基于位置的社交网络（LSBN）信息提要的重要方面。在过去的十年中，Foursquare，Twitter和Yelp等LSBN平台的蓬勃发展激发了大量针对POI建议的研究。受到深度神经网络在许多领域成功的启发，研究人员对使用诸如递归神经网络（RNN）之类的神经网络提出POI推荐越来越感兴趣。与传统方法如个性化马尔可夫链分解（FPMC）和张量分解（TF）等传统方法相比，神经网络方法在常规序列预测中显示出了很大的改进。但是，在个人作品中，对于个性化POI推荐至关重要的用户个人喜好在现有作品中并未得到很好的解决。而且，用户的个人偏好是动态的，而不是静态的，这可以指导不同时间和空间环境下的预测。为此，我们提出了一种新的深度神经网络模型，称为个人动态偏好神经网络（PDPNN）。 PDPNN模型的核心包括两部分：一部分从历史轨迹中学习用户的个人长期偏好，另一部分从当前轨迹中学习用户的短期偏好。通过引入一个相似度函数来评估用户当前轨迹的时空上下文和历史轨迹之间的相似性，PDPNN从用户的长期和短期偏好中学习用户的个人动态偏好。我们在三个现实世界的数据集上进行了实验，结果表明我们的模型优于目前众所周知的方法。

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