{"type":"ScholarlyArticle","title":"Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation","authors":[{"name":"Yang Li","slug":"yang-li","given":"Yang","family":"Li","affiliation":""},{"name":"Tong Chen","slug":"tong-chen","given":"Tong","family":"Chen","affiliation":""},{"name":"Yadan Luo","slug":"yadan-luo","given":"Yadan","family":"Luo","affiliation":""},{"name":"Hongzhi Yin","slug":"hongzhi-yin","given":"Hongzhi","family":"Yin","affiliation":""},{"name":"Zi Huang","slug":"zi-huang","given":"Zi","family":"Huang","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2021/206","url":"https://ijcai.org/proceedings/2021/206","pdf":"https://ijcai.org/proceedings/2021/0206.pdf","markdown":"https://ijcai.org/proceedings/2021/206.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/206","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Data Mining","track":"Main Track","number":206,"pages":{"first":1491,"last":1497},"keywords":["Data Mining: Mining Spatial, Temporal Data","Data Mining: Recommender Systems"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}