Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

Abstract

Next point-of-interest (POI) recommendation is an important personalized task in location-based social networks (LBSNs) and aims to recommend the next POI for users in a specific situation with historical check-in data. State-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural network (RNN) based models for modeling. However, these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate POIs. To address these limitations, a spatiotemporal trajectory (STT) model is proposed in this paper. We use the long short-term memory (LSTM) model with an attention mechanism as the basic framework and introduce the user’s spatiotemporal information into the model in encoding. In the process of encoding information, an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and distance. In addition, we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user’s current trajectory and the candidate set. We evaluate the performance of our STT model with four real-world datasets. Experimental results show that our model outperforms existing state-of-the-art methods.

摘要

下一个兴趣点(POI)推荐是基于位置的社交网络(LBSN)的一项重要任务, 其目标是使用历史签到数据在特定情境下为用户推荐下一个兴趣点。现有研究将用户时空信息线性离散化, 然后使用基于循环神经网络(RNN)的方法进行建模。但是这些研究忽略了时空信息对用户偏好的非线性影响以及用户轨迹和候选兴趣点之间的时空相关性。为解决这些问题, 本文提出一种时空轨迹(STT)模型。该模型使用具有注意力机制的长短期记忆网络(LSTM)作为基本框架, 并将时空信息以编码形式引入模型。在编码信息过程中, 使用指数型衰减因子刻画用户兴趣随时间和距离的非线性漂移特性。此外, 本文在目标召回过程中设计一个时空匹配模块, 该模块通过测量用户历史轨迹与候选集之间的相关性来为用户筛选最有可能的下一个兴趣点。本文使用4个真实数据集评估STT模型性能。实验结果表明, 本文所提方法的推荐效果比主流的推荐模型有显著提升。