DisenPOI: Disentangling Sequential and Geographical Influence for
Point-of-Interest Recommendation
- URL: http://arxiv.org/abs/2210.16591v2
- Date: Thu, 14 Sep 2023 08:59:03 GMT
- Title: DisenPOI: Disentangling Sequential and Geographical Influence for
Point-of-Interest Recommendation
- Authors: Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia
Cheng, Jun Lei, Ming Zhang
- Abstract summary: 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.
We propose DisenPOI, a novel Disentangled dual-graph framework for POI recommendation.
- Score: 17.729302810769415
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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