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Being an indispensable component in location-based social networks, nextpoint-of-interest (POI) recommendation recommends users unexplored POIs basedon their recent visiting histories. However, existing work mainly modelscheck-in data as isolated POI sequences, neglecting the crucial collaborativesignals from cross-sequence check-in information. Furthermore, the sparsePOI-POI transitions restrict the ability of a model to learn effectivesequential patterns for recommendation. In this paper, we proposeSequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowingcollaborative signals to be propagated from correlated POIs belonging to othersequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec),which jointly learns POI embeddings and infers a user's temporal preferencesfrom the graph-augmented POI sequence. To overcome the sparsity of POI-levelinteractions, we further infuse category-awareness into SGRec with a multi-tasklearning scheme that captures the denser category-wise transitions. As such,SGRec makes full use of the collaborative signals for learning expressive POIrepresentations, and also comprehensively uncovers multi-level sequentialpatterns for user preference modelling. Extensive experiments on two real-worlddatasets demonstrate the superiority of SGRec against state-of-the-art methodsin next POI recommendation.

Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation