## 6 Conclusions  
In this paper, we propose a novel POI recommendation model, MCGRec, which extracts POI representations from constructed contextual graphs. Unlike existing GNN-based methods, MCGRec leverages CNNs to extract POI features, offering greater expressive power compared to shallow GNN models. To apply the CNN model to graph-structured data, MCGRec introduces a super-node-based PPR sampling method, facilitating the sampling of POI nodes related to the target POI from the contextual association graph. Additionally, MCGRec incorporates a flexible CNN neural network module, ensuring the model's adaptability by allowing any reasonable combination of CNN layers. Finally, MCGRec develops a user preference estimation method that models user preferences using both geographical and temporal contextual features. We conduct extensive experiments on two real-world datasets, NYC and Gowalla. On the NYC dataset, MCGRec outperforms the second-best method by 8.5%, 7.5%, and 3.8% at Precision@5, Precision@10, and Precision@15, respectively. Additionally, MCGRec surpasses the second-best method by 5.7%, 3.4%, and 1.7% at Recall@5, Recall@10, and Recall@15, respectively. On the Gowalla dataset, MCGRec outperforms the second-best method by 15.3%, 9.3%, and 14.1% at Precision@5, Precision@10, and Precision@15, respectively. Furthermore, MCGRec surpasses the second-best method by 1.7%, 2.8%, and 2.1% at Recall@5, Recall@10, and Recall@15, respectively. These empirical results demonstrate the effectiveness of MCGRec on the POI recommendation task. A potential limitation of MCGRec lies in its naive use of the CNN architecture. Specifically, MCGRec employs only the basic modules of CNNs, overlooking more advanced and powerful architectures that could enhance the expressiveness of learned representations. Therefore, a promising future research direction is to integrate modern and powerful CNN architectures into MCGRec to further improve the model's performance. Conceptualization, S.J. and W.F. ; Data curation, W.F. and X.D. ; Formal analysis, X.D. ; Methodology, S.J. ; Project administration, W.F. ; Software, W.F. ; Writing--original draft, S.J. and W.F. ; Writing--review and editing, S.J., W.F. and X.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. [1][PERSON]; [PERSON]; [PERSON] STARec: Adaptive learning with spatiotemporal and activity influence for POI recommendation. _ACM Trans. Inf. Syst._**2021**, _40_, 65. [CrossRef]  
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