ToP: Time-dependent Zone-enhanced Points-of-interest Embedding-based Explainable Recommender system

Points-of-interest (POIs) recommendation plays a vital role by introducing unexplored POIs to consumers and has drawn extensive attention from both academia and industry. Existing POI recommender systems usually learn latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatiotemporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers complicated check-in actions. In this paper, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe POIs dynamic function. Besides, check-in actions to a POI is also affected by the zone it belongs to. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance the accuracy of POI recommendations. Along this line, we propose a Time-dependent Zone-enhanced POI embedding model (ToP), a recommender system that integrates knowledge graph and topic model to introduce the spatiotemporal effects into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in different time to capture its dynamic functions. Jointly combining these vectors with zones representations, ToP enhances the spatiotemporal interpretability of POI recommendations. With this hybrid architecture, some existing POI recommender systems can be treated as special cases of ToP. Extensive experiments on real-world Changchun city datasets demonstrate that ToP not only achieves state-of-the-art performance in terms of common metrics, but also provides more insights for consumers POI check-in actions.