Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation
نویسندگان
چکیده
Point of interest (POI) recommendation is an important task in location-based social networks. It plays a critical role smart tourism and makes it more likely for tourists to have personalized travel experiences. However, most current methods are based on learning the users’ check-in history relationship network make recommendations.Therefore, urban crowds’ regular patterns cannot be effectively utilized. In this paper, we propose POI algorithm (HMRec) prior knowledge human mobility solve problem. Specifically, Human Mobility Pattern Extraction (HMPE) framework, which utilizes graph neural networks as extractors patterns. The framework incorporates attention mechanisms capture spatio-temporal information traffic HMPE employs downstream tasks design upsampling modules reconstruct representation vectors objectives, enabling end-to-end training obtaining pre-trained parameters pattern extractor. Furthermore, introduce Recommendation algorithm, improves feature cross-interactions breadth model This ensures that results align closely with environments. Comparative experiments conducted Foursquare dataset demonstrate HMRec outperforms baseline models average performance improvement approximately 3%. Finally, discuss existing challenges future research directions, including approaches address issue data sparsity.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13116495