Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label of the dominant influence during recommendation, existing methods tend to entangle these two influences, which may lead to sub-optimal recommendation performance and poor interpretability. In this paper, we address the above challenge by proposing DisenPOI, a novel Disentangled dual-graph framework for POI recommendation, which jointly utilizes sequential and geographical relationships on two separate graphs and disentangles the two influences with self-supervision. The key novelty of our model compared with existing approaches is to extract disentangled representations of both sequential and geographical influences with contrastive learning. To be specific, we construct a geographical graph and a sequential graph based on the check-in sequence of a user. We tailor their propagation schemes to become sequence-/geo-aware to better capture the corresponding influences. Preference proxies are extracted from check-in sequence as pseudo labels for the two influences, which supervise the disentanglement via a contrastive loss. Extensive experiments on three datasets demonstrate the superiority of the proposed model.

翻译：利益点(POI)建议在不同的地点定位服务中发挥着关键作用,人们注意到,信息点建议是由顺序和地域影响驱动的,但由于没有在建议期间主要影响加注的标签,现有方法往往将这两种影响缠绕在一起,可能导致建议性能低于最佳水平,解释性差。在本文件中,我们通过提出DisenPOI建议来应对上述挑战。DisenPOI是一个关于信息点建议的新颖的分解双重绘图框架,它利用两个不同的图表的顺序和地理关系,将两种影响与自我监督混淆。与现有方法相比,我们模型的关键新颖之处是提取顺序和地域影响与对比性学习的分解表态。具体地说,我们根据用户的检查顺序构建一个地理图和顺序图。我们调整它们的传播计划,成为序列/地理意识,以更好地捕捉到相应的影响。将正比从检查顺序中提取出为两种影响假标签,以通过对比性模型来监督磁性变的模型。