TimeSAN: A Time-Modulated Self-Attentive Network for Next Point-of-Interest Recommendation
Jiayuan He, Jianzhong Qi, Kotagiri Ramamohanarao · 2020
Next Point-of-Interest (POI) recommendation aims to rank a list of POIs by their attractiveness to users based on the users' historical records of POI visits. This task is challenging, because user preferences may be influenced by various contextual factors. In this paper, we consider the temporal contextual factor, i.e., the time of users' POI visits. Previous attempts for modelling the impact of temporal contexts can be categorized into two groups: factorization based methods and recurrent neural network based methods. The first group adds a time dimension to their latent recommendation spaces, which may suffer from the data sparsity problem due to the additional dimension. The second group uses time-aware contextual gates to update the hidden and cell states in RNNs, which may have limited capability in capturing long-range temporal dynamics. In this paper, we propose a time -modulated s elf-attentive network (TimeSAN) for next POI recommendation. This model learns the relevance between a user's next POI visit and her historical visits via the self-attention mechanism, where the relevance is modulated by the temporal contextual influence. The learned time-aware relevance is further fused with users' long-term interests to provide final recommendations. We conduct extensive experiments on real-world datasets. The results confirm that TimeSAN outperforms previous methods consistently and significantly in recommendation accuracy, while attaining a high model training efficiency.