A Semantic Sequential Correlation Based LSTM Model for Next POI Recommendation

Abstract

The widespread of location-based social networks has generated massive check-in sequences in chronological order. Forecasting check-in sequences is significant while challenging due to the check-ins' sparsity problem. Existing methods have followed closely to incorporate spatial and temporal context to alleviate the data sparsity problem, but neglect the semantic sequential correlation between check-ins. Howbeit, incorporating the semantic sequential correlation between check-ins for next POI recommendation encounters the challenges of semantic sequential correlation measurement and sequential behavior modeling. To measure the semantic sequential correlation, we apply a semantic sequential correlation calculation model based on a semantic correlational graph that incorporates the time intervals' influence to calculate the semantic sequential correlation. Then, we apply a novel Long Short-Term Memory (LSTM) framework equipped with two additional semantic gates that takes the additional semantic sequential correlation as the extra input to capture users' sequential behaviors and model their long short-term interest with the restrictions in the semantic level. Finally, we cluster users into different groups as an improvement of our model to achieve a more accurate recommendation. Our proposed model is evaluated on a real-world and large-scale dataset and the experimental results demonstrate that our method outperforms the state-of-the-art methods for next POI recommendation.