LCE: A location category embedding model for predicting the category labels of POIs

Abstract

The proliferation of location-based social networks, makes it possible to record human mobility using an array of points-of-interest (POIs). Exploring the semantic meanings of POIs can be of great importance to many urban computing applications, e.g., personalized route recommendation and user trajectory clustering. Nonetheless, such information is not always available in practice. This paper aims at predicting the category labels, which will provide a succinct summarization of POIs. In particular, we first propose a Location Category Embedding (LCE) model, which projects user POIs and their associated category labels into the same vector space, and then identify the POIs’ most related category labels according to their similarities. To capture the influence that might affect users’ moving behavior, LCE considers sequential pattern, personal preference, and temporal influence, and further models the connection between the POIs and the three factors. Experimental results on two real-world datasets prove the effectiveness of the proposed method.