An Attentive Spatio-Temporal Neural Model for Successive Point of Interest Recommendation
| Autor: | Khoa Doan, Guolei Yang, Chandan K. Reddy |
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| Rok vydání: | 2019 |
| Předmět: |
Thesaurus (information retrieval)
Markov chain Point of interest business.industry Computer science Deep learning 02 engineering and technology Overfitting Machine learning computer.software_genre Matrix decomposition Recurrent neural network 020204 information systems 0202 electrical engineering electronic engineering information engineering Independence (mathematical logic) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Advances in Knowledge Discovery and Data Mining ISBN: 9783030161415 PAKDD (3) |
| DOI: | 10.1007/978-3-030-16142-2_27 |
| Popis: | In a successive Point of Interest (POI) recommendation problem, analyzing user behaviors and contextual check-in information in past POI visits are essential in predicting, thus recommending, where they would likely want to visit next. Although several works, especially the Matrix Factorization and/or Markov chain based methods, are proposed to solve this problem, they have strong independence and conditioning assumptions. In this paper, we propose a deep Long Short Term Memory recurrent neural network model with a memory/attention mechanism, for the successive Point-of-Interest recommendation problem, that captures both the sequential, and temporal/spatial characteristics into its learned representations. Experimental results on two popular Location-Based Social Networks illustrate significant improvements of our method over the state-of-the-art methods. Our method is also robust to overfitting compared with popular methods for the recommendation tasks. |
| Databáze: | OpenAIRE |
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