Abstract
Point-of-interest (POI) recommendations can help users explore attractive locations, which is playing an important role in location-based social networks (LBSNs). In POI recommendations, the results are largely impacted by users' preferences. However, the existing POI methods model user and location almost separately, which cannot capture users' personal and dynamic preferences to location. In addition, they also ignore users' acceptance to distance/time of location. To overcome the limitations of the existing methods, we first introduce Knowledge Graph with temporal information (known as TKG) into POI recommendation, including both user and location with timestamps. Then, based on TKG, we propose a Spatial-Temporal Graph Convolutional Attention Network (STGCAN), a novel network that learns users' preferences on TKG by dynamically capturing the spatial-temporal neighbourhoods. Specifically, in STGCAN, we construct receptive fields on TKG to aggregate neighbourhoods of user and location respectively at each timestamp. And we measure the spatial-temporal interval as users' acceptance to distance/time with self-attention. Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches.
Original language | English |
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Title of host publication | Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence |
Editors | Luc De Raedt |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 3530-3536 |
Number of pages | 7 |
ISBN (Electronic) | 9781956792003 |
DOIs | |
Publication status | Published - 2022 |
Event | 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 - Vienna, Austria Duration: 23 Jul 2022 → 29 Jul 2022 |
Conference
Conference | 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 |
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Country/Territory | Austria |
City | Vienna |
Period | 23/07/22 → 29/07/22 |