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.

本篇论文提出了一种基于双图的 POI 推荐方法 DisenPOI，旨在通过利用顺序和地理关系、自我监督等手段，将这两种影响区分开来， 实现更好的 POI 推荐效果。