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