LAUSR

Abstract Point-of-interest (POI) recommendation is an important technique widely used in self-driving services. While POI recommendation aims to recommend unvisited POIs to self-driving users, users always expect their intended items can be suggested together with these POIs, e.g. what activities to perform at the recommended places. However, existing methods cannot well support such POI recommendation in a finer granularity. In this paper, we investigate this new problem and propose a novel POI-based item recommendation model via graph embedding. The model accurately captures the joint effect of geographical and temporal influences on both POI-level and item-level recommendation in a shared space, which can address data sparsity and cold start problems effectively. To optimize the model efficiently and accurately, a novel weighted negative sampling strategy is designed. Besides, we propose a novel fine-grained user dynamic preference modeling method, which can accurately capture dynamic user preferences in a finer granularity based on the embeddings of both POIs and items. Comprehensive experimental studies have been conducted on three datasets. Results show that our model achieves significant improvement over state-of-the-art baselines.