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The task of point-of-interest (POI) recommendation aims to recommend locations to users in location-based applications. Among them, the task of time-aware POI recommendation aims to capture the user’s preferences that change dynamically over time, so as to make more accurate recommendations to users at a specific time. While existing works take into account the spatial, temporal and category context of POIs, they cannot capture user preferences that are more fine-grained than the category granularity. Additionally, RNN-based methods suffer from the problem of long-term dependency when capturing a user’s check-in patterns. To address these challenges, we propose a novel model with POI multi-grained grouping method which captures the user’s co-visit patterns and weekly patterns, to obtain finer-grained POI groups. The model also utilizes the transformer model to capture the user’s check-in preference …