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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