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Smart city article details
| Title | Beyond Fixed Time And Space: Next Poi Recommendation Via Multi-Grained Context And Correlation |
|---|---|
| ID_Doc | 11877 |
| Authors | Li X.; Hu R.; Wang Z. |
| Year | 2023 |
| Published | Neural Computing and Applications, 35, 1 |
| DOI | http://dx.doi.org/10.1007/s00521-022-07825-x |
| Abstract | POI recommendation is significant for discovering attractive locations, crime prediction, and smart city construction. Most existing methods only consider the fixed time and space between successive check-in points when capturing sequential patterns from trajectory history. However, single granularity is inadequate to mine the spatial-temporal influence on sequential patterns in sparse and incomplete check-in data. Besides, they neglect the relevance between non-adjacent check-ins and fail to fully exploit factors for the correlation mining. To tackle the above issues, we propose a novel model for the next POI recommendation via multi-granularity context and correlation. It focuses on exploring vital factors for modeling effective spatial-temporal contexts and mining potential correlations among check-ins. Specifically, for context modeling, we explore effective spatial-temporal contexts to learn mobility patterns locally and globally by introducing hierarchical regions and slots. For correlation modeling, we only focus on the geographical influence. We employ a spatial-aware function to measure the correlations among check-ins to find the predictive ones for the recommendation. Extensive experiments on widely used datasets indicate that our MGCOCO consistently and significantly outperforms the state-of-the-art approaches. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. |
| Author Keywords | Context; Correlation; Geographical Influence; Multi-grained; POI Recommendation |
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