Yuxia Wu
ABSTRACT
Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model users' general taste and recent sequential behavior. Moreover, the context information such as the category and check-in time is also important to capture user preference. To this end, we propose a long- and short-term preference learning model (LSPL) considering the sequential and context information. In long-term module, we learn the contextual features of POIs and leverage attention mechanism to capture users' preference. In the short-term module, we utilize LSTM to learn the sequential behavior of users. Specifically, to better learn the different influence of location and category of POIs, we train two LSTM models for location-based sequence and category-based sequence, respectively. Then we combine the long and short-term results to recommend next POI for users. At last, we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.
- huhui Jiang, Xueming Qian, Jialie Shen, et al. Author topic model-based collaborative filtering for personalized POI recommendations. IEEE transactions on multimedia, 2015, 17(6): 907--918.Google Scholar
- oshuai Zhao, Xiaojiang Lei, Xueming Qian, Tao Mei. Exploring Users' Internal Influence from Reviews for Social Recommendation.IEEE transactions on multimedia,2019, 21(3): 771--781Google Scholar
- teffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In Proc. WWW, 2010.Google Scholar
- teffen Rendle, Christoph Freudenthaler,Zeno Gantner, et al. BPR: Bayesian personalized ranking from implicit feedback. In Proc. AUAI, 2009: 452--461.Google Scholar
- epp Hochreiter, Jürgen Schmidhuber. Long short-term memory. Neural computation, 1997, 9(8): 1735--1780.Google Scholar
- ngliang Liao, Weiqing Liu, Yuan Zhong, et al. Predicting the next location: A recurrent model with spatial and temporal contexts.In Proc.AAAI. 2016.Google Scholar
- aochao Ying, Fuzheng Zhang, Yanchi Liu, et al. Sequential recommender system based on hierarchical attention networks. In Proc. IJCAI. 2018.Google Scholar
- ie Feng, Yong Li, Chao Zhang, et al. Deepmove: Predicting human mobility with attentional recurrent networks. In Proc. WWW, 2018: 1459--1468.Google Scholar
- ngqi Yang, Daqing Zhang, Vincent W. Zheng, et al. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014, 45(1): 129--142.Google Scholar
Index Terms
- Long- and Short-term Preference Learning for Next POI Recommendation
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