Nowadays, with the rise of location-based services, the personalized sequential POI recommendation has become a pivotal element for enhancing customer experiences. Although many previous POI recommendation models have shown promising results and improvements in this area, several challenges still exist in this field. Firstly, the previous sequential recommenders do not well -utilize the geographical features that are highly affecting the user's future choices of visits. Furthermore, the self-attention mechanism, which is a popular method used in sequential POI recommendation, has a limitation in treating the input user sequence as an unordered set.. Using positional embedding is a typical way to overcome this limitation. However, the use of such embeddings may potentially restrict the model's ability to learn meaningful patterns in user preferences among POIs. To address these challenges, we propose GeoMixer, a novel MLP-based sequential POI recommender that incorporates travel routing distance to capture geographical features and leverages Multi-layer Perceptron (MLP) architecture to model the spatial and sequential patterns in the sequential POI recommendations. By adopting MLP mixing layers, GeoMixer has the capability of memorizing the chronological order of the input POIs without the positional embedding and can emphasize the important latent features of each POI. The use of the travel routing information improves the model's ability of capturing spatial patterns during the model learning process. Extensive experiments on real world datasets show that GeoMixer outperforms state-of-theart methods in various metrics, highlighting the significance of incorporating travel routing distance and leveraging MIT architecture in sequential POI recommendation systems. Index Terms sequential recommendation, POI recommendation systems
