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：兴趣点推荐在各类位置感知服务中扮演着关键角色。研究表明，兴趣点推荐同时受序列影响和地理影响驱动。然而，由于推荐过程中缺乏主导影响的标注标签，现有方法往往将这两种影响纠缠在一起，这可能导致推荐性能次优且可解释性差。本文针对上述挑战，提出DisenPOI——一种新颖的解耦双图框架用于兴趣点推荐，该框架分别在两个独立图上联合利用序列关系和地理关系，并通过自监督方式解耦这两种影响。与现有方法相比，本模型的关键创新在于通过对比学习提取序列影响和地理影响的解耦表征。具体而言，我们基于用户的签到序列构建地理图与序列图，并设计其传播机制为序列感知型/地理感知型，以更好地捕获对应影响。从签到序列中提取偏好代理作为两种影响的伪标签，通过对比损失监督解耦过程。在三个数据集上的广泛实验证明了所提模型的优越性。