期刊
2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
卷 -, 期 -, 页码 1110-1115出版社
IEEE
DOI: 10.1109/ICDM.2018.00144
关键词
POI recommendation; attention mechanism; spatial and temporal; sequential prediction
资金
- National Key Research and Development Program of China [2018YFC0831604]
- NSFC [61602297, 61772341, 61472254, 61572324, 61170238, 61472241]
- Singapore NRF [CREATE E2S2]
- Program for Changjiang Young Scholars in University of China
- Program for Shanghai Top Young Talents
With the prosperity of the location-based social networks, next Point-of-Interest (POI) recommendation has become an important service and received much attention in recent years. The next POI is dynamically determined by the mobility pattern and various contexts associated with user check-in sequence. However, exploring spatial-temporal mobility patterns and incorporating heterogeneous contextual factors for recommendation are challenging issues to be resolved. In this paper, we introduce a novel neural network model named TMCA (Temporal and Multi-level Context Attention) for next POI recommendation. Our model employs the LSTM-based encoder-decoder framework, which is able to automatically learn deep spatial-temporal representations for historical check-in activities and integrate multiple contextual factors using the embedding method in a unified manner. We further propose the temporal and multilevel context attention mechanisms to adaptively select relevant check-in activities and contextual factors for next POI preference prediction. Extensive experiments have been conducted using two real-world check-in datasets. The results verify (1) the superior performance of our proposed method in different evaluation metrics, compared with several state-of-the-art methods; and (2) the effectiveness of the temporal and multi-level context attention mechanisms on recommendation performance.
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