Modeling Long and Short Term User Preferences by Leveraging Multi-Dimensional Auxiliary Information for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users’ check-in sequences, and are brittle to users with few interactions. Moreover, they ignore the impact of multi-dimen...

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Veröffentlicht in:ISPRS international journal of geo-information 2023-09, Vol.12 (9), p.352
Hauptverfasser: Li, Zheng, Huang, Xueyuan, Gong, Liupeng, Yuan, Ke, Liu, Chun
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Sprache:eng
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Zusammenfassung:Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users’ check-in sequences, and are brittle to users with few interactions. Moreover, they ignore the impact of multi-dimensional auxiliary information such as user check-in frequency, POI category on user preferences modeling and the impact of dynamic changes in user preferences over different time periods on recommendation performance. To address the above limitations, we propose a novel method for next POI recommendation by modeling long and short term user preferences with multi-dimensional auxiliary information. In particular, the proposed model includes a static LSTM module to capture users’ multi-dimensional long term static preferences and a dynamic meta-learning module to capture users’ multi-dimensional dynamic preferences. Furthermore, we incorporate a POI category filter into our model to comprehensively simulate users’ preferences. Experimental results on two real-world datasets demonstrate that our model outperforms the state-of-the-art baseline methods in two commonly used evaluation metrics.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi12090352