Abstract
The goal of next point of interest (POI) recommendation is to predict users’ next location by analyzing their check-in sequence. The next POI recommendation has attracted more research attention as an important application in location based social networks. Some next POI recommendation algorithms learn spatial and time difference for modelling user check-in sequence based on recurrent neural network (RNN) and its variants. However, these methods do not take into account the difference between the users’ long-term and short-term preferences, and cannot analyze the users’ historical preferences accurately without considering the properties of POI about category and comment comprehensively. In order to improve the precision of recommendation result, a long and short term preference model combined with contextual information (LSPMC) is proposed for next POI recommendation in this paper. Specifically, we take geographical information, temporal influence, the category and rating comment of POI into account to construct the input embedding vector. Three long and short term memory (LSTM) networks and attention mechanism are designed for modeling user long term preference. RNN model is used to implement user short term preference. The probability of POIs that user will visit next is obtained based on the long term preference and short term preference. The experimental results on two real datasets demonstrate that the proposed method performs better than other baseline methods in terms of four commonly evaluation indicators.
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Acknowledgements
This work is supported by the National Nature Science Foundation of China (62172186) and the Fundamental Research Funds for the Central Universities, JLU under Grant No.93K172021Z02.
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Leng, Z., Liu, Y., Zhou, X., Wang, X., Wang, X. (2022). A Long and Short Term Preference Model for Next Point of Interest Recommendation. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_61
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